System and method for image segmentation in generating computer models of a joint to undergo arthroplasty

ABSTRACT

Systems and methods for image segmentation in generating computer models of a joint to undergo arthroplasty are disclosed. Some embodiments may include a method of partitioning an image of a bone into a plurality of regions, where the method may include obtaining a plurality of volumetric image slices of the bone, generating a plurality of spline curves associated with the bone, verifying that at least one of the plurality of spline curves follow a surface of the bone, and creating a 3D mesh representation based upon the at least one of the plurality of spline curve.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(e) of U.S.Provisional Patent Application No. 61/126,102, entitled “System andMethod For Image Segmentation in Generating Computer Models of a Jointto Undergo Arthroplasty” filed on Apr. 30, 2008, which is herebyincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates to image segmentation. More specifically,the present invention relates to image segmentation in generatingcomputer models of a joint to undergo arthroplasty, wherein the computermodels may be used in the design and manufacture of arthroplasty jigs.

BACKGROUND OF THE INVENTION

Over time and through repeated use, bones and joints can become damagedor worn. For example, repetitive strain on bones and joints (e.g.,through athletic activity), traumatic events, and certain diseases(e.g., arthritis) can cause cartilage in joint areas, which normallyprovides a cushioning effect, to wear down. Cartilage wearing down canresult in fluid accumulating in the joint areas, pain, stiffness, anddecreased mobility.

Arthroplasty procedures can be used to repair damaged joints. During atypical arthroplasty procedure, an arthritic or otherwise dysfunctionaljoint can be remodeled or realigned, or an implant can be implanted intothe damaged region. Arthroplasty procedures may take place in any of anumber of different regions of the body, such as a knee, a hip, ashoulder, or an elbow.

One type of arthroplasty procedure is a total knee arthroplasty (“TKA”),in which a damaged knee joint is replaced with prosthetic implants. Theknee joint may have been damaged by, for example, arthritis (e.g.,severe osteoarthritis or degenerative arthritis), trauma, or a raredestructive joint disease. During a TKA procedure, a damaged portion inthe distal region of the femur may be removed and replaced with a metalshell, and a damaged portion in the proximal region of the tibia may beremoved and replaced with a channeled piece of plastic having a metalstem. In some TKA procedures, a plastic button may also be added underthe surface of the patella, depending on the condition of the patella.

Implants that are implanted into a damaged region may provide supportand structure to the damaged region, and may help to restore the damagedregion, thereby enhancing its functionality. Prior to implantation of animplant in a damaged region, the damaged region may be prepared toreceive the implant. For example, in a knee arthroplasty procedure, oneor more of the bones in the knee area, such as the femur and/or thetibia, may be treated (e.g., cut, drilled, reamed, and/or resurfaced) toprovide one or more surfaces that can align with the implant and therebyaccommodate the implant.

Accuracy in implant alignment is an important factor to the success of aTKA procedure. A one- to two-millimeter translational misalignment, or aone- to two-degree rotational misalignment, may result in imbalancedligaments, and may thereby significantly affect the outcome of the TKAprocedure. For example, implant misalignment may result in intolerablepost-surgery pain, and also may prevent the patient from having full legextension and stable leg flexion.

To achieve accurate implant alignment, prior to treating (e.g., cutting,drilling, reaming, and/or resurfacing) any regions of a bone, it isimportant to correctly determine the location at which the treatmentwill take place and how the treatment will be oriented. In some methods,an arthroplasty jig may be used to accurately position and orient afinishing instrument, such as a cutting, drilling, reaming, orresurfacing instrument on the regions of the bone. The arthroplasty jigmay, for example, include one or more apertures and/or slots that areconfigured to accept such an instrument.

A system and method has been developed for producing customizedarthroplasty jigs configured to allow a surgeon to accurately andquickly perform an arthroplasty procedure that restores thepre-deterioration alignment of the joint, thereby improving the successrate of such procedures. Specifically, the customized arthroplasty jigsare indexed such that they matingly receive the regions of the bone tobe subjected to a treatment (e.g., cutting, drilling, reaming, and/orresurfacing). The customized arthroplasty jigs are also indexed toprovide the proper location and orientation of the treatment relative tothe regions of the bone. The indexing aspect of the customizedarthroplasty jigs allows the treatment of the bone regions to be donequickly and with a high degree of accuracy that will allow the implantsto restore the patient's joint to a generally pre-deteriorated state.However, the system and method for generating the customized jigs oftenrelies on a human to “eyeball” bone models on a computer screen todetermine configurations needed for the generation of the customizedjigs. This “eyeballing” or manual manipulation of the bone modes on thecomputer screen is inefficient and unnecessarily raises the time,manpower and costs associated with producing the customized arthroplastyjigs. Furthermore, a less manual approach may improve the accuracy ofthe resulting jigs.

There is a need in the art for a system and method for reducing thelabor associated with generating customized arthroplasty jigs. There isalso a need in the art for a system and method for increasing theaccuracy of customized arthroplasty jigs.

SUMMARY

Systems and methods for image segmentation in generating computer modelsof a joint to undergo arthroplasty are disclosed. Some embodiments mayinclude a method of partitioning an image of a bone into a plurality ofregions, where the method of partitioning may include obtaining aplurality of volumetric image slices of the bone, generating a pluralityof spline curves associated with the bone, verifying that at least oneof the plurality of spline curves follow a surface of the bone, andcreating a three dimensional (3D) mesh representation based upon the atleast one of the plurality of spline curves.

Other embodiments may include a method of generating a representation ofa model bone, where the method of generating the representation mayinclude obtaining an image scan of the representation as a plurality ofslices, segmenting each slice in the plurality into one or moresegmentation curves, generating a mesh of the representation, adjustingeach slice in the plurality to include areas where the contact area ofthe bone is stable between successive image scans, and generating anchorsegmentation such that the anchor segmentation follows a boundary of therepresentation of the model bone.

Other embodiments may include a method of segmenting a target bone usinga representation of a model bone, where the method of segmenting thetarget bone may include registering a segmented form of therepresentation to an image scan of the target bone, refining theregistration of the segmented form of the representation near a boundaryof the target bone, generating a mesh from the segmented form of therepresentation, and generating a plurality of spline curves thatapproximate the intersection of the mesh and one or more slices from theimage scan of the target bone.

Other embodiments may include a method of mapping a representation of amodel bone into an image scan of a target bone, where the method ofmapping may include registering a generated portion of therepresentation into the image scan of the target bone using atranslational transformation, registering the generated portion of therepresentation into the image scan of the target bone using a similaritytransformation, registering a boundary portion of the representationinto the image scan of the target bone using an affine transformation,and registering the boundary portion of the representation into theimage scan of the target bone using a spline transformation.

Other embodiments may include a method for determining a degree ofcorrespondence between an image of a target bone and a representation ofa model bone, where the method of determining correspondence may includeselecting a plurality of sample points in the representation of themodel bone to be registered, partitioning the plurality of sample pointsinto a plurality of groups, sampling the image of the target bone,determining a correlation of voxel intensities between the image of thetarget bone and the representation of the model bone for each group inthe plurality, and averaging the correlation determined for each groupin the plurality.

Other embodiments may include a method for refining registration of arepresentation of a model bone to a target bone, where the method ofrefining may include transforming an anchor segmentation mesh,generating a plurality of random points around the transformed anchorsegmentation mesh, determining if each point in the plurality liesinside one or more of the following meshes: InDark-OutLight,InLight-OutDark, or Dark-In-Light, determining whether one or more ofthe plurality of points lie within a threshold distance of the surfaceof the transformed anchor segmentation mesh, and adding each point inthe plurality as a dark point or light point depending upon whether thepoint lies within the InDark-OutLight, InLight-OutDark, or Dark-In-Lightmeshes.

Still other embodiments may include a method for generating splinecurves outlining the surface of a feature of interest of a target bone,where the method of generating spline curves may include intersecting a3D mesh model of the feature surface with one or more slices of targetdata (the intersection defining a polyline curve), paramaterizing thepolyline curve as a function of length and tangent variation,calculating a weighted sum of the length and tangent paramaterizations,and sampling the polyline using the results of the act of calculating.

While multiple embodiments are disclosed, still other embodiments of thepresent invention will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments of the invention. As will be realized, theinvention is capable of modifications in various aspects, all withoutdeparting from the spirit and scope of the present invention.Accordingly, the drawings and detailed description are to be regarded asillustrative in nature and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof necessary fee.

FIG. 1A is a schematic diagram of a system for employing the automatedjig production method disclosed herein.

FIGS. 1B-1E are flow chart diagrams outlining the jig production methoddisclosed herein.

FIGS. 1F and 1G are, respectively, bottom and top perspective views ofan example customized arthroplasty femur jig.

FIGS. 1H and 1I are, respectively, bottom and top perspective views ofan example customized arthroplasty tibia jig.

FIG. 2A is a sagittal plane image slice depicting a femur and tibia andneighboring tissue regions with similar image intensity.

FIG. 2B is a sagittal plane image slice depicting a region extendinginto the slice from an adjacent image slice.

FIG. 2C is a sagittal plane image slice depicting a region of a femurthat is approximately tangent to the image slice.

FIG. 3A is a sagittal plane image slice depicting an intensity gradientacross the slice.

FIG. 3B is a sagittal plane image slice depicting another intensitygradient across the slice.

FIG. 3C is a sagittal plane image slice depicting another intensitygradient across the slice.

FIG. 4A depicts a sagittal plane image slice with a high noise level.

FIG. 4B depicts a sagittal plane image slice with a low noise level.

FIG. 5 is a sagittal plane image slice of a femur and tibia depictingregions where good definition may be needed during automaticsegmentation of the femur and tibia.

FIG. 6 depicts a flowchart illustrating one method for automaticsegmentation of an image modality scan of a patient's knee joint.

FIG. 7A is a sagittal plane image slice of a segmented femur.

FIG. 7B is a sagittal plane image slice of a segmented femur and tibia.

FIG. 7C is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7D is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7E is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7F is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7G is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7H is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7I is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7J is another sagittal plane image slice of a segmented femur andtibia.

FIG. 7K is another sagittal plane image slice of a segmented femur andtibia.

FIG. 8 is a sagittal plane image slice depicting automatically generatedslice curves of a femur and a tibia.

FIG. 9 depicts a 3D mesh geometry of a femur.

FIG. 10 depicts a 3D mesh geometry of a tibia.

FIG. 11 depicts a flowchart illustrating one method for generating agolden template.

FIG. 12A is a sagittal plane image slice depicting a contour curveoutlining a golden tibia region, a contour curve outlining a grown tibiaregion and a contour curve outlining a boundary golden tibia region.

FIG. 12B is a sagittal plane image slice depicting a contour curveoutlining a golden femur region, a contour curve outlining a grown femurregion and a contour curve outlining a boundary golden femur region.

FIG. 13A depicts a golden tibia 3D mesh.

FIG. 13B depicts a golden femur 3D mesh.

FIG. 14A is a sagittal plane image slice depicting anchor segmentationregions of a tibia.

FIG. 14B is a sagittal plane image slice depicting anchor segmentationregions of a femur.

FIG. 15A is a 3D mesh geometry depicting the anchor segmentation mesh,the InDark-OutLight anchor mesh, the InLight-OutDark anchor mesh, andthe Dark-In-Light anchor mesh of a tibia.

FIG. 15B is a 3D mesh geometry depicting the anchor segmentation mesh,the InDark-OutLight anchor mesh and the InLight-OutDark anchor mesh of afemur.

FIG. 16 depicts a flowchart illustrating one method for performingautomatic segmentation of scan data using golden template registration.

FIG. 17 depicts a flowchart illustrating one method for mapping thesegmented golden femur template regions into the target scan data usingimage registration techniques.

FIG. 18 depicts a registration framework that may be employed by oneembodiment.

FIG. 19 depicts a flowchart illustrating one method for mapping thesegmented golden tibia template regions into the target scan data usingimage registration techniques.

FIG. 20 depicts a flowchart illustrating one method for computing ametric for the registration framework of FIG. 18.

FIG. 21 depicts a flowchart illustrating one method for refining theregistration results using anchor segmentation and anchor regions.

FIG. 22 depicts a set of randomly generated light sample points and darksample points of a tibia.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves to outline features of interest in each target MRI slice.

FIG. 24 depicts a polyline curve with n vertices.

FIG. 25 depicts a flowchart illustrating one method for adjustingsegments.

FIG. 26 is a sagittal plane image slice depicting a contour curve withcontrol points outlining a femur with superimposed contour curves of thefemur from adjacent image slices.

FIG. 27 depicts a 3D slice visualization of a femur showing the voxelsinside of the spline curves.

DETAILED DESCRIPTION

Disclosed herein are customized arthroplasty jigs 2 and systems 4 for,and methods of, producing such jigs 2. The jigs 2 are customized to fitspecific bone surfaces of specific patients. Depending on the embodimentand to a greater or lesser extent, the jigs 2 are automatically plannedand generated and may be similar to those disclosed in these three U.S.patent applications: U.S. patent application Ser. No. 11/656,323 to Parket al., titled “Arthroplasty Devices and Related Methods” and filed Jan.19, 2007; U.S. patent application Ser. No. 10/146,862 to Park et al.,titled “Improved Total Joint Arthroplasty System” and filed May 15,2002; and U.S. patent Ser. No. 11/642,385 to Park et al., titled“Arthroplasty Devices and Related Methods” and filed Dec. 19, 2006. Thedisclosures of these three U.S. Patent Applications are incorporated byreference in their entireties into this Detailed Description.

a. Overview of System and Method for Manufacturing CustomizedArthroplasty Cutting Jigs

For an overview discussion of the systems 4 for, and methods of,producing the customized arthroplasty jigs 2, reference is made to FIGS.1A-1E. FIG. 1A is a schematic diagram of a system 4 for employing theautomated jig production method disclosed herein. FIGS. 1B-1E are flowchart diagrams outlining the jig production method disclosed herein. Thefollowing overview discussion can be broken down into three sections.

The first section, which is discussed with respect to FIG. 1A and[blocks 100-125] of FIGS. 1B-1E, pertains to an example method ofdetermining, in a three-dimensional (“3D”) computer model environment,saw cut and drill hole locations 30, 32 relative to 3D computer modelsthat are termed restored bone models 28. In some embodiments, theresulting “saw cut and drill hole data” 44 is referenced to the restoredbone models 28 to provide saw cuts and drill holes that will allowarthroplasty implants to restore the patient's joint to itspre-degenerated state. In other words, in some embodiments, thepatient's joint may be restored to its natural alignment, whethervalgus, varus or neutral.

While many of the embodiments disclosed herein are discussed withrespect to allowing the arthroplasty implants to restore the patient'sjoint to its pre-degenerated or natural alignment state, many of theconcepts disclosed herein may be applied to embodiments wherein thearthroplasty implants restore the patient's joint to a zero mechanicalaxis alignment such that the patient's knee joint ends up being neutral,regardless of whether the patient's predegenerated condition was varus,valgus or neutral. Accordingly, this disclosure should not be limited tomethods resulting in natural alignment only, but should, whereappropriate, be considered as applicable to methods resulting in zeromechanical axis.

The second section, which is discussed with respect to FIG. 1A and[blocks 100-105 and 130-145] of FIGS. 1B-1E, pertains to an examplemethod of importing into 3D computer generated jig models 38 3D computergenerated surface models 40 of arthroplasty target areas 42 of 3Dcomputer generated arthritic models 36 of the patient's joint bones. Theresulting “jig data” 46 is used to produce a jig customized to matinglyreceive the arthroplasty target areas of the respective bones of thepatient's joint.

The third section, which is discussed with respect to FIG. 1A and[blocks 150-165] of FIG. 1E, pertains to a method of combining orintegrating the “saw cut and drill hole data” 44 with the “jig data” 46to result in “integrated jig data” 48. The “integrated jig data” 48 isprovided to the CNC machine 10 for the production of customizedarthroplasty jigs 2 from jig blanks 50 provided to the CNC machine 10.The resulting customized arthroplasty jigs 2 include saw cut slots anddrill holes positioned in the jigs 2 such that when the jigs 2 matinglyreceive the arthroplasty target areas of the patient's bones, the cutslots and drill holes facilitate preparing the arthroplasty target areasin a manner that allows the arthroplasty joint implants to generallyrestore the patient's joint line to its pre-degenerated state.

As shown in FIG. 1A, the system 4 includes one or more computers 6having a CPU 7, a monitor or screen 9 and an operator interface controls11. The computer 6 is linked to a medical imaging system 8, such as a CTor MRI machine 8, and a computer controlled machining system 10, such asa CNC milling machine 10.

As indicated in FIG. 1A, a patient 12 has a joint 14 (e.g., a knee,elbow, ankle, wrist, hip, shoulder, skull/vertebrae orvertebrae/vertebrae interface, etc.) to be replaced. The patient 12 hasthe joint 14 scanned in the imaging machine 8. The imaging machine 8makes a plurality of scans of the joint 14, wherein each scan pertainsto a thin slice of the joint 14.

As can be understood from FIG. 1B, the plurality of scans is used togenerate a plurality of two-dimensional (“2D”) images 16 of the joint 14[block 100]. Where, for example, the joint 14 is a knee 14, the 2Dimages will be of the femur 18 and tibia 20. The imaging may beperformed via CT or MRI. In one embodiment employing MRI, the imagingprocess may be as disclosed in U.S. patent application Ser. No.11/946,002 to Park, which is entitled “Generating MRI Images Usable ForThe Creation Of 3D Bone Models Employed To Make Customized ArthroplastyJigs,” was filed Nov. 27, 2007 and is incorporated by reference in itsentirety into this Detailed Description.

As can be understood from FIG. 1A, the 2D images are sent to thecomputer 6 for creating computer generated 3D models. As indicated inFIG. 1B, in one embodiment, point P is identified in the 2D images 16[block 105]. In one embodiment, as indicated in [block 105] of FIG. 1A,point P may be at the approximate medial-lateral and anterior-posteriorcenter of the patient's joint 14. In other embodiments, point P may beat any other location in the 2D images 16, including anywhere on, nearor away from the bones 18, 20 or the joint 14 formed by the bones 18,20.

As described later in this overview, point P may be used to locate thecomputer generated 3D models 22, 28, 36 created from the 2D images 16and to integrate information generated via the 3D models. Depending onthe embodiment, point P, which serves as a position and/or orientationreference, may be a single point, two points, three points, a point plusa plane, a vector, etc., so long as the reference P can be used toposition and/or orient the 3D models 22, 28, 36 generated via the 2Dimages 16.

As shown in FIG. 1C, the 2D images 16 are employed to create computergenerated 3D bone-only (i.e., “bone models”) 22 of the bones 18, 20forming the patient's joint 14 [block 110]. The bone models 22 arelocated such that point P is at coordinates (X_(0-j), Y_(0-j), Z_(0-j))relative to an origin (X₀, Y₀, Z₀) of an X-Y-Z axis [block 110]. Thebone models 22 depict the bones 18, 20 in the present deterioratedcondition with their respective degenerated joint surfaces 24, 26, whichmay be a result of osteoarthritis, injury, a combination thereof, etc.

Computer programs for creating the 3D computer generated bone models 22from the 2D images 16 include: Analyze from AnalyzeDirect, Inc.,Overland Park, Kans.; Insight Toolkit, an open-source software availablefrom the National Library of Medicine Insight Segmentation andRegistration Toolkit (“ITK”), www.itk.org; 3D Slicer, an open-sourcesoftware available from www.slicer.org; Mimics from Materialise, AnnArbor, Mich.; and Paraview available at www.paraview.org. Further, someembodiments may use customized software such as OMSegmentation,developed by OtisMed, Inc. The OMSegmentation software may extensivelyuses “ITK” and/or “VTK”. Some embodiments may include using a prototypeof OMSegmentation, and as such may utilize InsightSNAP software.

As indicated in FIG. 1C, the 3D computer generated bone models 22 areutilized to create 3D computer generated “restored bone models” or“planning bone models” 28 wherein the degenerated surfaces 24, 26 aremodified or restored to approximately their respective conditions priorto degeneration [block 115]. Thus, the bones 18, 20 of the restored bonemodels 28 are reflected in approximately their condition prior todegeneration. The restored bone models 28 are located such that point Pis at coordinates (X_(0-j), Y_(0-j), Z_(0-j)) relative to the origin(X₀, Y₀, Z₀). Thus, the restored bone models 28 share the sameorientation and positioning relative to the origin (X₀, Y₀, Z₀) as thebone models 22.

In one embodiment, the restored bone models 28 are manually created fromthe bone models 22 by a person sitting in front of a computer 6 andvisually observing the bone models 22 and their degenerated surfaces 24,26 as 3D computer models on a computer screen 9. The person visuallyobserves the degenerated surfaces 24, 26 to determine how and to whatextent the degenerated surfaces 24, 26 surfaces on the 3D computer bonemodels 22 need to be modified to restore them to their pre-degeneratedcondition. By interacting with the computer controls 11, the person thenmanually manipulates the 3D degenerated surfaces 24, 26 via the 3Dmodeling computer program to restore the surfaces 24, 26 to a state theperson believes to represent the pre-degenerated condition. The resultof this manual restoration process is the computer generated 3D restoredbone models 28, wherein the surfaces 24′, 26′ are indicated in anon-degenerated state.

In one embodiment, the bone restoration process is generally orcompletely automated. In other words, a computer program may analyze thebone models 22 and their degenerated surfaces 24, 26 to determine howand to what extent the degenerated surfaces 24, 26 surfaces on the 3Dcomputer bone models 22 need to be modified to restore them to theirpre-degenerated condition. The computer program then manipulates the 3Ddegenerated surfaces 24, 26 to restore the surfaces 24, 26 to a stateintended to represent the pre-degenerated condition. The result of thisautomated restoration process is the computer generated 3D restored bonemodels 28, wherein the surfaces 24′, 26′ are indicated in anon-degenerated state.

As depicted in FIG. 1C, the restored bone models 28 are employed in apre-operative planning (“POP”) procedure to determine saw cut locations30 and drill hole locations 32 in the patient's bones that will allowthe arthroplasty joint implants to generally restore the patient's jointline to it pre-degenerative alignment [block 120].

In one embodiment, the POP procedure is a manual process, whereincomputer generated 3D implant models 34 (e.g., femur and tibia implantsin the context of the joint being a knee) and restored bone models 28are manually manipulated relative to each other by a person sitting infront of a computer 6 and visually observing the implant models 34 andrestored bone models 28 on the computer screen 9 and manipulating themodels 28, 34 via the computer controls 11. By superimposing the implantmodels 34 over the restored bone models 28, or vice versa, the jointsurfaces of the implant models 34 can be aligned or caused to correspondwith the joint surfaces of the restored bone models 28. By causing thejoint surfaces of the models 28, 34 to so align, the implant models 34are positioned relative to the restored bone models 28 such that the sawcut locations 30 and drill hole locations 32 can be determined relativeto the restored bone models 28.

In one embodiment, the POP process is generally or completely automated.For example, a computer program may manipulate computer generated 3Dimplant models 34 (e.g., femur and tibia implants in the context of thejoint being a knee) and restored bone models or planning bone models 28relative to each other to determine the saw cut and drill hole locations30, 32 relative to the restored bone models 28. The implant models 34may be superimposed over the restored bone models 28, or vice versa. Inone embodiment, the implant models 34 are located at point P′ (X_(0-k),Y_(0-k), Z_(0-k)) relative to the origin (X₀, Y₀, Z₀), and the restoredbone models 28 are located at point P (X_(0-j), Y_(0-j), Z_(0-j)). Tocause the joint surfaces of the models 28, 34 to correspond, thecomputer program may move the restored bone models 28 from point P(X_(0-j), Y_(0-j), Z_(0-j)) to point P′ (X_(0-k), Y_(0-k), Z_(0-k)), orvice versa. Once the joint surfaces of the models 28, 34 are in closeproximity, the joint surfaces of the implant models 34 may beshape-matched to align or correspond with the joint surfaces of therestored bone models 28. By causing the joint surfaces of the models 28,34 to so align, the implant models 34 are positioned relative to therestored bone models 28 such that the saw cut locations 30 and drillhole locations 32 can be determined relative to the restored bone models28.

As indicated in FIG. 1E, in one embodiment, the data 44 regarding thesaw cut and drill hole locations 30, 32 relative to point P′ (X_(0-k),Y_(0-k), Z_(0-k)) is packaged or consolidated as the “saw cut and drillhole data” 44 [block 145]. The “saw cut and drill hole data” 44 is thenused as discussed below with respect to [block 150] in FIG. 1E.

As can be understood from FIG. 1D, the 2D images 16 employed to generatethe bone models 22 discussed above with respect to [block 110] of FIG.1C are also used to create computer generated 3D bone and cartilagemodels (i.e., “arthritic models”) 36 of the bones 18, 20 forming thepatient's joint 14 [block 130]. Like the above-discussed bone models 22,the arthritic models 36 are located such that point P is at coordinates(X_(0-j), Y_(0-j), Z_(0-j)) relative to the origin (X₀, Y₀, Z₀) of theX-Y-Z axis [block 130]. Thus, the bone and arthritic models 22, 36 sharethe same location and orientation relative to the origin (X₀, Y₀, Z₀).This position/orientation relationship is generally maintainedthroughout the process discussed with respect to FIGS. 1B-1E.Accordingly, movements relative to the origin (X₀, Y₀, Z₀) of the bonemodels 22 and the various descendants thereof (i.e., the restored bonemodels 28, bone cut locations 30 and drill hole locations 32) are alsoapplied to the arthritic models 36 and the various descendants thereof(i.e., the jig models 38). Maintaining the position/orientationrelationship between the bone models 22 and arthritic models 36 andtheir respective descendants allows the “saw cut and drill hole data” 44to be integrated into the “jig data” 46 to form the “integrated jigdata” 48 employed by the CNC machine 10 to manufacture the customizedarthroplasty jigs 2.

Computer programs for creating the 3D computer generated arthriticmodels 36 from the 2D images 16 include: Analyze from AnalyzeDirect,Inc., Overland Park, Kans.; Insight Toolkit, an open-source softwareavailable from the National Library of Medicine Insight Segmentation andRegistration Toolkit (“ITK”), www.itk.org; 3D Slicer, an open-sourcesoftware available from www.slicer.org; Mimics from Materialise, AnnArbor, Mich.; and Paraview available at www.paraview.org. Someembodiments may use customized software such as OMSegmentation,developed by OtisMed, Inc. The OMSegmentation software may extensivelyuses “ITK” and/or “VTK”. Also, some embodiments may include using aprototype of OMSegmentation, and as such may utilize InsightSNAPsoftware.

Similar to the bone models 22, the arthritic models 36 depict the bones18, 20 in the present deteriorated condition with their respectivedegenerated joint surfaces 24, 26, which may be a result ofosteoarthritis, injury, a combination thereof, etc. However, unlike thebone models 22, the arthritic models 36 are not bone-only models, butinclude cartilage in addition to bone. Accordingly, the arthritic models36 depict the arthroplasty target areas 42 generally as they will existwhen the customized arthroplasty jigs 2 matingly receive thearthroplasty target areas 42 during the arthroplasty surgical procedure.

As indicated in FIG. 1D and already mentioned above, to coordinate thepositions/orientations of the bone and arthritic models 36, 36 and theirrespective descendants, any movement of the restored bone models 28 frompoint P to point P′ is tracked to cause a generally identicaldisplacement for the “arthritic models” 36 [block 135].

As depicted in FIG. 1D, computer generated 3D surface models 40 of thearthroplasty target areas 42 of the arthritic models 36 are importedinto computer generated 3D arthroplasty jig models 38 [block 140]. Thus,the jig models 38 are configured or indexed to matingly receive thearthroplasty target areas 42 of the arthritic models 36. Jigs 2manufactured to match such jig models 38 will then matingly receive thearthroplasty target areas of the actual joint bones during thearthroplasty surgical procedure.

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is a manual process. The 3D computergenerated models 36, 38 are manually manipulated relative to each otherby a person sitting in front of a computer 6 and visually observing thejig models 38 and arthritic models 36 on the computer screen 9 andmanipulating the models 36, 38 by interacting with the computer controls11. In one embodiment, by superimposing the jig models 38 (e.g., femurand tibia arthroplasty jigs in the context of the joint being a knee)over the arthroplasty target areas 42 of the arthritic models 36, orvice versa, the surface models 40 of the arthroplasty target areas 42can be imported into the jig models 38, resulting in jig models 38indexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. Point P′ (X_(0-k), Y_(0-k), Z_(0-k)) can also beimported into the jig models 38, resulting in jig models 38 positionedand oriented relative to point P′ (X_(0-k), Y_(0-k), Z_(0-k)) to allowtheir integration with the bone cut and drill hole data 44 of [block125].

In one embodiment, the procedure for indexing the jig models 38 to thearthroplasty target areas 42 is generally or completely automated, asdisclosed in U.S. patent application Ser. No. 11/959,344 to Park, whichis entitled System and Method for Manufacturing Arthroplasty Jigs, wasfiled Dec. 18, 2007 and is incorporated by reference in its entiretyinto this Detailed Description. For example, a computer program maycreate 3D computer generated surface models 40 of the arthroplastytarget areas 42 of the arthritic models 36. The computer program maythen import the surface models 40 and point P′ (X_(0-k), Y_(0-k),Z_(0-k)) into the jig models 38, resulting in the jig models 38 beingindexed to matingly receive the arthroplasty target areas 42 of thearthritic models 36. The resulting jig models 38 are also positioned andoriented relative to point P′ (X_(0-k), Y_(0-k), Z_(0-k)) to allow theirintegration with the bone cut and drill hole data 44 of [block 125].

In one embodiment, the arthritic models 36 may be 3D volumetric modelsas generated from a closed-loop process. In other embodiments, thearthritic models 36 may be 3D surface models as generated from anopen-loop process.

As indicated in FIG. 1E, in one embodiment, the data regarding the jigmodels 38 and surface models 40 relative to point P′ (X_(0-k), Y_(0-k),Z_(0-k)) is packaged or consolidated as the “jig data” 46 [block 145].The “jig data” 46 is then used as discussed below with respect to [block150] in FIG. 1E.

As can be understood from FIG. 1E, the “saw cut and drill hole data” 44is integrated with the “jig data” 46 to result in the “integrated jigdata” 48 [block 150]. As explained above, since the “saw cut and drillhole data” 44, “jig data” 46 and their various ancestors (e.g., models22, 28, 36, 38) are matched to each other for position and orientationrelative to point P and P′, the “saw cut and drill hole data” 44 isproperly positioned and oriented relative to the “jig data” 46 forproper integration into the “jig data” 46. The resulting “integrated jigdata” 48, when provided to the CNC machine 10, results in jigs 2: (1)configured to matingly receive the arthroplasty target areas of thepatient's bones; and (2) having cut slots and drill holes thatfacilitate preparing the arthroplasty target areas in a manner thatallows the arthroplasty joint implants to generally restore thepatient's joint line to its pre-degenerated state.

As can be understood from FIGS. 1A and 1E, the “integrated jig data” 44is transferred from the computer 6 to the CNC machine 10 [block 155].Jig blanks 50 are provided to the CNC machine 10 [block 160], and theCNC machine 10 employs the “integrated jig data” to machine thearthroplasty jigs 2 from the jig blanks 50.

For a discussion of example customized arthroplasty cutting jigs 2capable of being manufactured via the above-discussed process, referenceis made to FIGS. 1F-1I. While, as pointed out above, the above-discussedprocess may be employed to manufacture jigs 2 configured forarthroplasty procedures involving knees, elbows, ankles, wrists, hips,shoulders, vertebra interfaces, etc., the jig examples depicted in FIGS.1F-1I are for total knee replacement (“TKR”) or partial knee replacement(“PKR”) procedures. Thus, FIGS. 1F and 1G are, respectively, bottom andtop perspective views of an example customized arthroplasty femur jig2A, and FIGS. 1H and 1I are, respectively, bottom and top perspectiveviews of an example customized arthroplasty tibia jig 2B.

As indicated in FIGS. 1F and 1G, a femur arthroplasty jig 2A may includean interior side or portion 100 and an exterior side or portion 102.When the femur cutting jig 2A is used in a TKR or PKR procedure, theinterior side or portion 100 faces and matingly receives thearthroplasty target area 42 of the femur lower end, and the exteriorside or portion 102 is on the opposite side of the femur cutting jig 2Afrom the interior portion 100.

The interior portion 100 of the femur jig 2A is configured to match thesurface features of the damaged lower end (i.e., the arthroplasty targetarea 42) of the patient's femur 18. Thus, when the target area 42 isreceived in the interior portion 100 of the femur jig 2A during the TKRor PKR surgery, the surfaces of the target area 42 and the interiorportion 100 match.

The surface of the interior portion 100 of the femur cutting jig 2A ismachined or otherwise formed into a selected femur jig blank 50A and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged lower end or target area 42 of the patient's femur 18.

As indicated in FIGS. 1H and 1I, a tibia arthroplasty jig 2B may includean interior side or portion 104 and an exterior side or portion 106.When the tibia cutting jig 2B is used in a TKR or PKR procedure, theinterior side or portion 104 faces and matingly receives thearthroplasty target area 42 of the tibia upper end, and the exteriorside or portion 106 is on the opposite side of the tibia cutting jig 2Bfrom the interior portion 104.

The interior portion 104 of the tibia jig 2B is configured to match thesurface features of the damaged upper end (i.e., the arthroplasty targetarea 42) of the patient's tibia 20. Thus, when the target area 42 isreceived in the interior portion 104 of the tibia jig 2B during the TKRor PKR surgery, the surfaces of the target area 42 and the interiorportion 104 match.

The surface of the interior portion 104 of the tibia cutting jig 2B ismachined or otherwise formed into a selected tibia jig blank 50B and isbased or defined off of a 3D surface model 40 of a target area 42 of thedamaged upper end or target area 42 of the patient's tibia 20.

b. Automatic Segmentation of Scanner Modality Image Data to Generate 3DSurface Model of a Patient's Bone

In one embodiment, the 2D images 16 of the patient's joint 14 asgenerated via the imaging system 8 (see FIG. 1A and [block 100] of FIG.1B) are analyzed to identify the contour lines of the bones and/orcartilage surfaces that are of significance with respect to generating3D models 22, 36, as discussed above with respect to [blocks 110 and130] of FIGS. 1C and 1D. Specifically, a variety of image segmentationprocesses may occur with respect to the 2D images 16 and the dataassociated with such 2D images 16 to identify contour lines that arethen compiled into 3D bone models, such as bone models 22 and arthriticmodels 36. A variety of processes and methods for performing imagesegmentation are disclosed in the remainder of this DetailedDescription.

The imager 8 typically generates a plurality of image slices 16 viarepetitive imaging operations. Depending on whether the imager 8 is aMRI or CT imager, each image slice will be a MRI or CT slice. As shownin FIG. 2A, the image slice may depict the cancellous bone 200, thecortical bone 202 surrounding the cancellous bone, and the articularcartilage lining portions of the cortical bone 202 of an object ofinterest of a joint, e.g., a femur 204 in a patient's knee joint 14. Theimage may further depict the cancellous bone 206, the cortical bone 208of another object of interest in the joint, e.g., a tibia 210 of theknee joint 14. In one embodiment, each image slice 16 may be atwo-millimeter 2D image slice.

One embodiment may automatically segment one or more features ofinterest (e.g., bones) present in MRI or CT scans of a patient joint,e.g., knee, hip, elbow, etc. A typical scan of a knee joint mayrepresent approximately a 100-millimeter by 150-millimeter by150-millimeter volume of the joint and may include about 40 to 80 slicestaken in sagittal planes. A sagittal plane is an imaginary plane thattravels from the top to the bottom of the object (e.g., the human body),dividing it into medial and lateral portions. It is to be appreciatedthat a large inter-slice spacing may result in voxels (volume elements)with aspect ratios of about one to seven between the resolution in thesagittal plane (e.g., the y z plane) and the resolution along the x axis(i.e., each scan slice lies in the yz plane with a fixed value of x).For example, a two-millimeter slice that is 150-millimeters by150-millimeters may be comprised of voxels that are approximately0.3-millimeter by 0.3-millimeter by 2-millimeters (for a 512 by 512image resolution in the sagittal plane).

In one embodiment, each slice may be a gray scale image with aresolution of 512 by 512 voxels where the voxel value represents thebrightness (intensity) of the voxel. The intensity may be stored as a16-bit integer resulting in an intensity range from 0 to 65,535, where 0may represent black and 65,535 may represent white. The intensity ofeach voxel typically represents the average intensity of the voxelvolume. Other embodiments may employ scans having higher or lowerresolutions in the sagittal plane, different inter-slice spacing, orimages where the intensity may be represented by a 24 bit vector (e.g.,eight bits each for a red component, green component and bluecomponent). Additionally, other embodiments may store intensity valuesas 32-bit signed integers or floating point values.

Typical MRI and CT scan data generally provide images where parts of abone boundary of interest may be well defined while other parts of thebone boundary may be difficult to determine due to voxel volumeaveraging, the presence of osteophyte growth, the presence of tissuehaving similar image intensities in neighboring areas to the object tobe segmented, amongst other things. Such poor definition of parts of thebone boundary in the images may cause traditional automated segmentationtechniques to fail. For example, FIG. 2A depicts regions 212 within aslice where an object boundary may not be visible due to neighboringtissue having about the same intensity as the feature of interest.Depicted in FIG. 2B are regions 214 that may be extended into the slicefrom adjacent slices due to a high voxel aspect ratio. Depicted in FIG.2C is a region 216 of the bone boundary 218 that may disappear or loseregularity when the bone boundary 218 is approximately tangent to theslice.

One embodiment may employ image segmentation techniques using a goldentemplate to segment bone boundaries and provide improved segmentationresults over traditional automated segmentation techniques. Suchtechniques may be used to segment an image when similarity betweenpixels within an object to be identified may not exist. That is, thepixels within a region to be segmented may not be similar with respectto some characteristic or computed property such as a color, intensityor texture that may be employed to associate similar pixels intoregions. Instead, a spatial relationship of the object with respect toother objects may be used to identify the object of interest. In oneembodiment, a 3D golden template of a feature of interest to besegmented may be used during the segmentation process to locate thetarget feature in a target scan. For example, when segmenting a scan ofa knee joint, a typical 3D image of a known good femur (referred to as agolden femur template) may be used to locate and outline (i.e., segment)a femur in a target scan.

Generally, much of the tissues surrounding the cancellous and corticalmatter of the bone to be segmented may vary from one MRI scan to anotherMRI scan. This may be due to disease and/or patient joint position(e.g., a patient may not be able to straighten the joint of interestbecause of pain). By using surrounding regions that have a stableconnection with the bone (e.g., the grown golden and boundary goldenregions of the template as described in more detail below), theregistration may be improved. Additionally, use of these regions allowthe bone geometry of interest to be captured during the segmentationrather than other features not of interest. Further, the segmentationtakes advantage of the higher resolution of features of interest incertain directions of the scan data through the use of a combination of2D and 3D techniques that selectively increases the precision of thesegmentation as described in more detail below with respect to refiningthe bone registration using an artificially generated image.

The segmentation method employed by one embodiment may accommodate avariety of intensity gradients across the scan data. FIGS. 3A-C depictintensity gradients (i.e., the intensity varies non-uniformly across theimage) in slices (an intensity gradient that is darker on the top andbottom as depicted in FIG. 3A, an intensity gradient that is darker onthe bottom as depicted in FIG. 3B, and an intensity gradient 220 that isbrighter on the sides as depicted in FIG. 3C) that may be segmented byone embodiment. Further, the embodiment generally does not requireapproximately constant noise in the slices to be segmented. Theembodiment may accommodate different noise levels, e.g., high noiselevels as depicted in FIG. 4A as well as low noise levels as depicted inFIG. 4B. The decreased sensitivity to intensity gradients and noiselevel typically is due to image registration techniques using a goldentemplate, allowing features of interest to be identified even though thefeature may include voxels with differing intensities and noise levels.

Segmentation generally refers to the process of partitioning a digitalimage into multiple regions (e.g., sets of pixels for a 2D image or setsof voxels in a 3D image). Segmentation may be used to locate features ofinterest (bones, cartilage, ligaments, etc.) and boundaries (lines,curves, etc. that represent the bone boundary or surface) in an image.In one embodiment, the output of the automatic segmentation of the scandata may be a set of images (scan slices 16) where each image 16includes a set of extracted closed contours representing bone outlinesthat identify respective bone location and shape for bones of interest(e.g., the shape and location of the tibia and femur in the scan data ofa knee joint). The automatic segmentation of a joint image slices 16 tocreate 3D models (e.g., bone models 22 and arthritic models 36) of thesurface of the bones in the joint may reduce the time required tomanufacture customized arthroplasty cutting jigs 2. It is to beappreciated that certain embodiments may generate open contours of thebone shapes of interest to further reduce computation time.

In one embodiment, scan protocols may be chosen to provide gooddefinition in areas where precise geometry reconstruction is requiredand to provide lower definition in areas that are not as important forgeometry reconstruction. The automatic image segmentation of oneembodiment employs components whose parameters may be tuned for thecharacteristics of the image modality used as input to the automaticsegmentation and for the features of the anatomical structure to besegmented, as described in more detail below.

In one embodiment, a General Electric 3T MRI scanner may be used toobtain the scan data. The scanner settings may be set as follows: pulsesequence: FRFSE-XL Sagittal PD; 3 Pane Locator—Scout Scan Thickness:4-millimeters; Imaging Options: TRF, Fast, FR; Gradient Mode: Whole; TE:approximately 31; TR: approximately 2100; Echo Train Length: 8;Bandwidth: 50 Hz; FOV: 16 centimeters, centered at the joint line; PhaseFOV: 0.8 or 0.9; Slice Thickness: 2 millimeters; Spacing: Interleave;Matrix: 384×192; NEX: 2; Frequency: SI; and Phase Correct: On. It is tobe appreciated that other scanners and settings may be used to generatethe scan data.

Typically, the voxel aspect ratio of the scan data is a function of howmany scan slices may be obtained while a patient remains immobile. Inone embodiment, a two-millimeter inter-slice spacing may be used duringa scan of a patient's knee joint. This inter-slice spacing providessufficient resolution for constructing 3D bone models of the patient'sknee joint and may be taken of the joint before the patient moves.

FIG. 5 depicts a MRI scan slice that illustrates image regions wheregood definition may be needed during automatic segmentation of theimage. Typically, this may be areas where the bones come in contactduring knee motion, in the anterior shaft area next to the joint andareas located at about a 10- to 30-millimeter distance from the joint.Good definition may be needed in regions 230 of the tibia 232 andregions 234 of the femur 236. Regions 238 depict areas where the tibiais almost tangent to the slice and boundary information may be lost dueto voxel volume averaging.

Voxel volume averaging may occur during the data acquisition processwhen the voxel size is larger than a feature detail to be distinguished.For example, the detail may have a black intensity while the surroundingregion may have a white intensity. When the average of the contiguousdata enclosed in the voxel is taken, the average voxel intensity valuemay be gray. Thus, it may not be possible to determine in what part ofthe voxel the detail belongs.

Regions 240 depict areas where the interface between the cortical boneand cartilage is not clear (because the intensities are similar), orwhere the bone is damaged and may need to be restored, or regions wherethe interface between the cancellous bone and surrounding region may beunclear due to the presence of a disease formation (e.g., an osteophytegrowth which has an image intensity similar to the adjacent region).

FIG. 6 depicts a flowchart illustrating one method for automaticsegmentation of an image modality scan (e.g., an MRI scan) of apatient's knee joint. Initially, operation 250 obtains a scan of thepatient's knee joint. In one embodiment, the scan may include about 50sagittal slices. Other embodiments may use more or fewer slices. Eachslice may be a gray scale image having a resolution of 512 by 512voxels. The scan may represent approximately a 100-millimeter by150-millimeter by 150-millimeter volume of the patient's knee. While theinvention will be described for an MRI scan of a knee joint, this is byway of illustration and not limitation. The invention may be used tosegment other types of image modality scans such as computed tomography(CT) scans, ultrasound scans, positron emission tomography (PET) scans,etc., as well as other joints including, but not limited to, hip joints,elbow joints, etc. Further, the resolution of each slice may be higheror lower and the images may be in color rather than gray scale. It is tobe appreciated that transversal or coronal slices may be used in otherembodiments.

After operation 250 obtains scan data (e.g., scan images 16) generatedby imager 8, operation 252 may be performed to segment the femur data ofthe scan data. During this operation, the femur may be located andspline curves 270 may be generated to outline the femur shape or contourlines in the scan slices, as depicted in FIGS. 7A-7K. It should beappreciated that one or more spline curves may be generated in eachslice to outline the femur contour depending on the shape and curvatureof the femur as well as the femur orientation relative to the slicedirection.

Next, in operation 254, a trained technician may verify that thecontours of the femur spline curves generated during operation 252follow the surface of the femur bone. The technician may determine thata spline curve does not follow the bone shape in a particular slice. Forexample, FIG. 8 depicts an automatically generated femur spline curve274. The technician may determine that the curve should be enlarged inthe lower left part 276 of the femur because it is worn out in thisregion and may need reconstruction. The technician may determine this byexamining the overall 3D shape of the segmented femur and also bycomparing lateral and medial parts of the scan data. The segmentedregion of the slice may be enlarged by dragging one or more controlpoints 278 located on the spline curve 274 to adjust the curve to moreclosely follow the femur boundary as determined by the technician, asshown by adjusted curve 280. The number of control points on a splinecurve may be dependent on the curve length and curvature variations.Typically, 10-25 control points may be associated with a spline curvefor spline modification.

Once the technician is satisfied with all of the femur spline curves inthe scan slices, operation 256 generates a watertight triangular meshgeometry from the femur segmentation that approximates the 3D surface ofthe femur. The mesh closely follows the femur spline curves 270 andsmoothly interpolates between them to generate a 3D surface model of thefemur. FIG. 9 depicts a typical 3D mesh geometry 290 of a target femurgenerated by one embodiment. Such a 3D model may be a 3D surface modelor 3D volume model resulting from open-loop contour lines or closed loopcontour lines, respectively. In one embodiment, such a 3D model asdepicted in FIG. 9 may be a bone model 22 or an arthritic model 36.

After operation 256, operation 258 may be performed to segment the tibiadata in the scan data. During this operation, the tibia is located andspline curves may be generated to locate and outline the shape of thetibia found in the scan slices, as depicted by tibia spline curves 272in FIGS. 7A-7K. It should be appreciated that one or more spline curvesmay be generated in each slice to outline the tibia depending on theshape and curvature of the tibia as well as the tibia orientationrelative to the slice direction.

Next, in operation 260, the technician may verify the tibia splinecurves generated during operation 258. The technician may determine thata spline curve does not follow the tibia in a particular slice. Forexample, referring back to FIG. 8, an automatically generated tibiaspline curve 282 is depicted that may not follow the tibia in the rightpart of the tibia due to the presence of an osteophyte growth 284. Thepresence of the osteophyte growth 284 may be determined by examiningneighboring slices. In this case, the segmented region may be reduced bydragging one or more control points 286 located on the spline curve tomodify the tibia spline curve 282 to obtain the adjusted tibia splinecurve 288. As previously discussed, each spline curve may haveapproximately 10-25 control points depending on the length and curvaturevariation of the spline curve.

Once the technician is satisfied with all of the tibia spline curves inthe scan slices, operation 262 generates a watertight triangular meshgeometry from the tibia segmentation. The mesh closely follows thespline curves and smoothly interpolates between them to generate a 3Dsurface model of the tibia. FIG. 10 depicts a typical 3D mesh geometry292 of a target tibia generated by one embodiment. Such a 3D model maybe a 3D surface model or 3D volume model resulting from open-loopcontour lines or closed loop contour lines, respectively. In oneembodiment, such a 3D model as depicted in FIG. 10 may be a bone model22 or an arthritic model 36.

Because the objects to be located in the scan data typically cannot besegmented by grouping similar voxels into regions, a golden templaterepresentative of a typical size and shape of the feature of interestmay be employed during the segmentation process to locate the targetfeature of interest.

FIG. 11 depicts a flowchart illustrating one method for generating agolden template. The method will be described for generating a goldentemplate of a tibia by way of illustration and not limitation. Themethod may be used to generate golden templates of other bonesincluding, but not limited to a femur bone, a hip bone, etc.

Initially, operation 300 obtains a scan of a tibia that is not damagedor diseased. The appropriate tibia scan may be chosen by screeningmultiple MRI tibia scans to locate a MRI tibia scan having a tibia thatdoes not have damaged cancellous and cortical matter (i.e., no damage intibia regions that will be used as fixed images to locate acorresponding target tibia in a target scan during segmentation), whichhas good MRI image quality, and which has a relatively average shape,e.g., the shaft width relative to the largest part is not out ofproportion (which may be estimated by eye-balling the images). Thistibia scan data, referred to herein as a golden tibia scan, may be usedto create a golden tibia template. It is to be appreciated that severalMRI scans of a tibia (or other bone of interest) may be selected, atemplate generated for each scan, statistics gathered on the successrate when using each template to segment target MRI scans, and the onewith the highest success rate selected as the golden tibia template.

Then, in operation 302 the tibia is segmented in each scan slice. Eachsegmentation region includes the cancellous matter 322 and corticalmatter 324 of the tibia, but excludes any cartilage matter to form agolden tibia region, outlined by a contour curve 320, as depicted inFIG. 12A.

Next, operation 304 generates a golden tibia mesh 340 from theaccumulated golden tibia contours of the image slices, as illustrated inFIG. 13A.

Next, operation 306 increases the segmented region in each slice bygrowing the region to include boundaries between the tibia and adjacentstructures where the contact area is generally relatively stable fromone MRI scan to another MRI scan. This grown region may be referred toherein as a grown golden tibia region, outlined by contour curve 328, asdepicted in FIG. 12A.

The grown golden region may be used to find the surface that separatesthe hard bone (cancellous and cortical) from the outside matter(cartilage, tendons, water, etc.). The changes in voxel intensities whengoing from inside the surface to outside of the surface may be used todefine the surface. The grown golden region may allow the registrationprocess to find intensity changes in the target scan that are similar tothe golden template intensity changes near the surface. Unfortunately,the golden segmentation region does not have stable intensity changes(e.g., near the articular surface) or may not have much of an intensitychange. Thus, the grown region typically does not include such regionsbecause they do not provide additional information and may slow down theregistration due to an increased number of points to be registered.

Finally, use of a grown golden region may increase the distance wherethe metric function detects a feature during the registration process.When local optimization is used, the registration may be moved in aparticular direction only when a small movement in that directionimproves the metric function. When a golden template feature is fartheraway from the corresponding target bone feature (e.g., when there is asignificant shape difference), the metric typically will not move towardthat feature. Use of the larger grown region may allow the metric todetect the feature and move toward it.

Next, operation 308 cuts off most of the inner part of the grown goldentibia region to obtain a boundary golden tibia region 330 depicted inFIG. 12A. The boundary golden tibia region 330 is bounded on the insideby contour curve 332 and the outside by contour curve 328.

The boundary region may be used to obtain a more precise registration ofthe target bone by using the interface from inside the inside hard boneto the outside hard bone. This may be done so that intensity variationsin other areas (e.g., intensity variations deep inside the bone) thatmay move the registration toward wrong features and decrease theprecision of locating the hard bone surface are not used during theregistration.

Then, operation 310 applies Gaussian smoothing with a standard deviationof two pixels to every slice of the golden tibia scan. In oneembodiment, a vtkimageGaussianSmooth filter (part of VisualizationToolKit, a free open source software package) may be used to perform theGaussian smoothing by setting the parameter “Standard Deviation” to avalue of two.

Then, operation 312 generates an anchor segmentation. The anchorsegmentation typically follows the original segmentation where the tibiaboundary is well defined in most MRI scans. In areas where the tibiaboundary may be poorly defined, but where there is another well definedfeature close to the tibia boundary, the anchor segmentation may followthat feature instead. For example, in an area where a healthy bonenormally has cartilage, a damaged bone may or may not have cartilage. Ifcartilage is present in this damaged bone region, the bone boundaryseparates the dark cortical bone from the gray cartilage matter. Ifcartilage is not present in this area of the damaged bone, there may bewhite liquid matter next to the dark cortical bone or there may beanother dark cortical bone next to the damaged bone area. Thus, theinterface from the cortical bone to the outside matter in this region ofthe damage bone typically varies from MRI scan to MRI scan. In suchareas, the interface between the cortical and the inner cancellous bonemay be used. These curves may be smoothly connected together in theremaining tibia areas to obtain the tibia anchor segmentation curve 358,depicted in FIG. 14A.

Then, operation 314 may determine three disjoint regions along theanchor segmentation boundary. Each of these regions is generally welldefined in most MRI scans. FIG. 14A depicts these three disjoint regionsfor a particular image slice. The first region 350, referred to hereinas the tibia InDark-OutLight region, depicts a region where the anchorsegmentation boundary separates the inside dark intensity corticalmatter voxels from the outside light intensity cancellous matter voxels.The second region 352, referred to herein as the tibia InLight-OutDarkregion, depicts a region where the boundary separates the inside lightintensity cancellous matter voxels from the outside dark intensitycortical matter voxels. Finally, region 354, referred to herein as thetibia Dark-in-Light region, depicts a region that has a very thin layerof dark intensity cortical matter voxels along the boundary, but whichhas light intensity cancellous matter voxels away from the boundary(i.e., on both sides of the boundary). Generally, the other regionsalong the anchor segmentation boundary vary from scan to scan or may notbe clear in most of the scans, as depicted by regions 356. Such regionsmay be an osteophyte growth with an arbitrary shape but which has aboutthe same intensity as the region next to it. Thus, such regionstypically are not used as anchor regions in one embodiment of theinvention.

Finally, operation 316 generates a mesh corresponding to the anchorsegmentation and also generates a mesh for each anchor region. FIG. 15Adepicts the anchor segmentation mesh 360, the InDark-OutLight anchorregion mesh 362, the InLight-OutDark anchor region mesh 364 and theDark-in-Light anchor region mesh 366 for the tibia. These 3D meshesmodel the surface of the golden tibia in the specified regions. It is tobe appreciated that the 3D meshes are distinct and generally are notcombined to create a composite mesh. These meshes may be used to createan artificial fixed image that is used during the registration processas described in more detail below.

A golden template of a femur may also be generated in a similar mannerusing the method depicted by FIG. 11. FIG. 12B depicts the golden femurregion, outlined by a contour curve 320A, the grown femur region,outlined by contour curve 328A, and the boundary golden femur region330A bounded on the inside by contour curve 332A and the outside bycontour curve 328A. FIG. 13B depicts the golden femur mesh 340A. FIG.14B depicts the femur anchor segmentation curve 358A, the femurInDark-OutLight region 350A and the femur InLight-OutDark region 352A.Finally, FIG. 15B depicts the anchor segmentation mesh 360A, theInDark-OutLight anchor region mesh 362A and the InLight-OutDark anchorregion mesh 364A for the femur.

FIG. 16 depicts a flowchart illustrating one method for performingautomatic segmentation (e.g., operation 252 or operation 258 of FIG. 6)of the scan data of a joint (e.g., a MRI scan of a knee joint) usinggolden template registration. The segmentation method may be used tosegment the femur (operation 252 of FIG. 6) and/or the tibia (operation258 of FIG. 6) in either the left or right knee. Different goldentemplate data may be used to segment the left tibia, right tibia, leftfemur or right femur. Additionally, other embodiments may segment otherjoints, including but not limited to, hip joints, elbow joints, by usingan appropriate golden template of the feature of interest to besegmented.

Initially, operation 370 maps the segmented golden template and markedregions (e.g., grown and boundary regions) to the target scan data usingimage registration techniques. This may be done to locate thecorresponding feature of interest in the target scan (e.g., a targetfemur or tibia). In one embodiment, a 3D golden template may be mappedto the target scan data. Registration transforms the template imagecoordinate system into the target coordinate system. This allows thetemplate image to be compared and/or integrated with the target image.

Next, operation 372 refines the registration near the feature (e.g., abone) boundary of interest. Anchor segmentation and anchor regions maybe used with a subset of 3D free-form deformations to move points withinthe plane of the slices (e.g., the yz plane) but not transversal (alongthe x axis) to the slices. Refinement of the initial registrationoperation may be necessary to correct errors caused by a high voxelaspect ratio. When a point from a golden template is mapped onto thetarget scan, it generally maps to a point between adjacent slices of thescan data. For example, if a translation occurs along the x direction,then the point being mapped may only align with a slice when thetranslation is a multiple of the inter-slice scan distance (e.g., amultiple of two-millimeters for an inter-slice spacing oftwo-millimeters). Otherwise, the point will be mapped to a point thatfalls between slices. In such cases, the intensity of the target scanpoint may be determined by averaging the intensities of correspondingpoints (voxels) in the two adjacent slices. This may further reduceimage resolution. Additionally, refinement of the initial registrationoperation may correct for errors due to unhealthy areas and/or limitedcontrast areas. That is, the golden template may be partially pulledaway from the actual bone boundary in diseased areas and/or minimalcontrast areas (e.g., toward a diseased area having a differentcontrast) during the initial registration operation.

Next, operation 374 generates a polygon mesh representation of thesegmented scan data. A polygon mesh typically is a collection ofvertices, edges, and faces that may define the surface of a 3D object.The faces may consist of triangles, quadrilaterals or other simpleconvex polygons. In one embodiment, a polygon mesh may be generated byapplying the registration transform found during operation 372 to allthe vertices of a triangle golden template mesh (i.e., the surface ofthe mesh is composed of triangular faces). It is to be appreciated thatthe cumulative registration transform typically represents the transformthat maps the golden template into the target MRI scan with minimalmisalignment error.

Finally, operation 376 generates spline curves that approximate theintersection of the mesh generated by operation 374 with the target MRIslices. Note that these spline curves may be verified by the technician(during operation 254 or operation 260 of FIG. 6).

FIG. 17 depicts a flowchart illustrating one method for mapping thesegmented golden femur template regions into the target scan using imageregistration techniques. Registration may be thought of as anoptimization problem with a goal of finding a spatial mapping thataligns a fixed image with a target image. Generally several registrationoperations may be performed, first starting with a coarse imageapproximation and a low-dimensional transformation group to find a roughapproximation of the actual femur location and shape. This may be doneto reduce the chance of finding wrong features instead of the femur ofinterest. For example, if a free-form deformation registration wasinitially used to register the golden femur template to the target scandata, the template might be registered to the wrong feature, e.g., to atibia rather than the femur of interest. A coarse registration may alsobe performed in less time than a fine registration, thereby reducing theoverall time required to perform the registration. Once the femur hasbeen approximately located using a coarse registration, finerregistration operations may be performed to more accurately determinethe femur location and shape. By using the femur approximationdetermined by the prior registration operation as the initialapproximation of the femur in the next registration operation, the nextregistration operation may find a solution in less time.

In one embodiment, each registration operation may employ a registrationframework 390 as depicted in FIG. 18. The registration framework 390 mayemploy an image similarity-based method. Such a method generallyincludes a transformation model T(X) 392, which may be applied tocoordinates of a fixed (or reference) image 394 (e.g., a golden femurtemplate) to locate their corresponding coordinates in a target image396 space (e.g., a MRI scan), an image similarity metric 398, whichquantifies the degree of correspondence between features in both imagespaces achieved by a given transformation, and an optimizer 400, whichtries to maximize image similarity (or minimize an opposite function) bychanging the parameters of the transformation model 392. An interpolator402 may be used to evaluate target image intensities at non-gridlocations (e.g., reference image points that are mapped to target imagepoints that lie between slices). Thus, a registration frameworktypically includes two input images, a transform, a metric, aninterpolator and an optimizer.

Referring again to FIG. 17, operation 380 may approximately register agrown femur region in a MRI scan using a coarse registrationtransformation. In one embodiment, this may be done by performing anexhaustive translation transform search on the MRI scan data to identifythe appropriate translation transform parameters that minimizestranslation misalignment of the reference image femur mapped onto thetarget femur of the target image. This coarse registration operationtypically determines an approximate femur position in the MRI scan.During this operation, the femur of the reference image may beoverlapped with the target femur of the target image using a translationtransformation to minimize translational misalignment of the femurs.

A translational transform, translates (or shifts) an image by the same3D vector. That is, the reference femur may be mapped into the targetimage space by shifting the reference femur along one or more axes inthe target image space to minimize misalignment. During this operationthe reference femur is not rotated, scaled or deformed. In oneembodiment, three parameters for the translation transformation may begenerated, one parameter for each dimension that specifies thetranslation for that dimension. The final parameters of the translationtransform minimizing the misalignment of the mapped reference femurimage coordinates into the target image space may be stored.

Next, operation 382 further refines the image registration determined byoperation 380. This may be done by approximately registering the grownfemur region of the reference golden template femur into the target MRIscan data using a similarity transformation. In one embodiment, asimilarity transformation may be performed in 3D space. The referencegolden femur region may be rotated in 3D, translated in 3D andhomogeneously scaled to map its coordinates into the target MRI scandata to minimize misalignment between the reference golden femur regionand the corresponding region in the target MRI scan. In someembodiments, a center of rotation may be specified so that both therotation and scaling operations are performed with respect to thespecified center of rotation. In one embodiment, a 3D similaritytransformation, specified by seven parameters, may be used. Oneparameter specifies the scaling factor, three parameters specify aversor that represents the 3D rotation and three parameters specify avector that represents the 3D translation in each dimension. A versor isa unit quanternion that provides a convenient mathematical notation forrepresenting orientations and rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference golden femur region onto the target MRI scan that is not farfrom the registration of the reference golden femur region onto thetarget MRI scan found in the previous operation 190 and used as theinitial starting approximation. Registering the grown golden femurregion may increase the distance where the metric function detects afeature during the registration process. When local optimization isused, the registration may be moved in a particular direction only whena small movement in that direction improves the metric function. When agolden femur template feature is farther away from the correspondingtarget femur feature (e.g., when there is a significant shapedifference), the metric typically will not move toward that feature. Useof the larger grown femur region may allow the metric to detect thefeature and move toward it.

After operation 382, operation 384 further refines the imageregistration of the golden femur into the target scan. In oneembodiment, an affine transformation may be used to register coordinatesof a boundary golden femur region of a golden femur template into thetarget MRI scan data. In one embodiment, the approximate femurregistration found during operation 382 may be used as the initialstarting approximation for the affine transformation.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe boundary golden femur region mapped into the target MRI scan datamay be stored.

Finally, operation 386 further refines the image registration of theboundary golden femur region. In one embodiment, a spline transformationmay be used to register the coordinates of the boundary golden femurregion into the MRI scan data (target image space). In one embodiment, a3D B-Spline deformable transformation may be employed and thetransformation found in operation 384 may be used as the initialtransformation values for the 3D B-Spline deformable transformation.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. For example, a 3D B-splinedeformable transform T may specify a 3D vector V(P) for every point P inthe original 3D space that is moved by T such that T:P→P+V(P).

In one embodiment, a B-Spline transformation may be specified with M×Nparameters, where M is the number of nodes in the B-Spline grid and N isthe dimension of the space. In one embodiment, a 3D B-Spline deformabletransformation of order three may be used to map every reference image3D point into the target MRI scan by a different 3D vector. The field ofthe vectors may be modeled using B-splines. Typically a grid J×K×L ofcontrol points may be specified where J, K, and L are parameters of thetransformation.

In one embodiment, splines of order three may be used with a grid 9×6×6of control points. That is, the transformation employs nine controlpoints in the medial/lateral direction (i.e., the x direction), and sixcontrol points in the other directions (i.e., y and z directions). Threecontrol points in each dimension (i.e., 3 of 9 in the x direction, 3 of6 in the y direction and 3 of 6 in the z direction) may be used tospecify boundary conditions. As such, the inner spline nodes may form agrid of size 6 by 3 by 3 and the boundary conditions increase the gridto size 9 by 6 by 6. The parametric set for this transformation has adimension of 3×9×6×6=972 (i.e., each dimension may have a 9×6×6 grid ofcontrol points). The final parameters of the spline transformation thatminimizes the misalignment between the reference golden femur templateand the target MRI scan data may be stored. This may be referred to asthe cumulative femur registration transform herein.

FIG. 19 depicts a flowchart illustrating one method for mapping thesegmented golden tibia template regions into the target scan using imageregistration techniques. Generally several registration operations maybe performed, first starting with a coarse image approximation and alow-dimensional transformation group to find a rough approximation ofthe actual tibia location and shape. This may be done to reduce thechance of finding wrong features instead of the tibia of interest. Forexample, if a free-form deformation registration was initially used toregister the golden tibia template to the target scan data, the templatemight be registered to the wrong feature, e.g., to a femur rather thanthe tibia of interest. A coarse registration may also be performed inless time than a fine registration, thereby reducing the overall timerequired to perform the registration. Once the tibia has beenapproximately located using a coarse registration, finer registrationoperations may be performed to more accurately determine the tibialocation and shape. By using the tibia approximation determined by theprior registration operation as the initial approximation of the tibiain the next registration operation, the next registration operation mayfind a solution in less time.

In one embodiment, each registration operation may employ a registrationframework 390 as depicted in FIG. 18. The registration framework 390 mayemploy an image similarity-based method. Such a method generallyincludes a transformation model T(X) 392, which may be applied tocoordinates of a fixed (or reference) image 394 (e.g., a golden tibiatemplate) to locate their corresponding coordinates in a target image396 space (e.g., a MRI scan), an image similarity metric 398, whichquantifies the degree of correspondence between features in both imagespaces achieved by a given transformation, and an optimizer 400, whichtries to maximize image similarity by changing the parameters of thetransformation model 392. An interpolator 402 may be used to evaluatetarget image intensities at non-grid locations (i.e., reference imagepoints that are mapped to target image points that lie between slices).Thus, a registration framework typically includes two input images, atransform, a metric, an interpolator and an optimizer.

The automatic segmentation registration process will be described usingscan data that includes a right tibia bone. This is by way ofillustration and not limitation. Referring again to FIG. 19, operation410 may approximately register a grown tibia region in a MRI scan usinga coarse registration transformation. In one embodiment, this may bedone by performing an exhaustive translation transform search on the MRIscan data to identify the appropriate translation transform parametersthat minimizes translation misalignment of the reference image tibiamapped onto the target tibia of the target image. This coarseregistration operation typically determines an approximate tibiaposition in the MRI scan. During this operation, the tibia of thereference image may be overlapped with the target tibia of the targetimage using a translation transformation to minimize translationalmisalignment of the tibias.

A translational transform, translates (or shifts) an image by the same3D vector. That is, the reference tibia may be mapped into the targetimage space by shifting the reference tibia along one or more axes inthe target image space to minimize misalignment. During this operationthe reference tibia is not rotated, scaled or deformed. In oneembodiment, three parameters for the translation transformation may begenerated, one parameter for each dimension that specifies thetranslation for that dimension. The final parameters of the translationtransform minimizing the misalignment of the mapped reference tibiaimage coordinates into the target image space may be stored.

Next, operation 412 further refines the image registration determined byoperation 410. This may be done by approximately registering the growntibia region of the reference golden tibia template into the target MRIscan data using a similarity transformation. In one embodiment, asimilarity transformation may be performed in 3D space. The referencegolden tibia region may be rotated in 3D, translated in 3D andhomogeneously scaled to map its coordinates into the target MRI scandata to minimize misalignment between the reference golden tibia regionand the corresponding region in the target MRI scan. In someembodiments, a center of rotation may be specified so that both therotation and scaling operations are performed with respect to thespecified center of rotation. In one embodiment, a 3D similaritytransformation, specified by seven parameters, may be used. Oneparameter specifies the scaling factor, three parameters specify aversor that represents the 3D rotation and three parameters specify avector that represents the 3D translation in each dimension. A versor isa unit quanternion that provides a convenient mathematical notation forrepresenting orientations and rotations of objects in three dimensions.

In one embodiment, local minimization techniques may be employed withthe similarity transformation to obtain a refined registration of thereference golden tibia region onto the target MRI scan that is not farfrom the registration of the reference golden tibia region onto thetarget MRI scan found in the previous operation 410 and used as theinitial starting approximation. Registering the grown golden tibiaregion may increase the distance where the metric function detects afeature during the registration process. When local optimization isused, the registration may be moved in a particular direction only whena small movement in that direction improves the metric function. When agolden tibia template feature is farther away from the correspondingtarget tibia feature (e.g., when there is a significant shapedifference), the metric typically will not move toward that feature. Useof the larger grown tibia region may allow the metric to detect thefeature and move toward it.

After operation 412, operation 414 further refines the imageregistration. In one embodiment, an affine transformation may be used toregister coordinates of a boundary golden tibia region of a golden tibiatemplate into the target MRI scan data. In one embodiment, theapproximate tibia registration found during operation 412 may be used asthe initial starting approximation for the affine transformation.

An affine transformation typically is a linear transformation followedby a translation. The affine transformation preserves collinearitybetween points (i.e., three points which lie on a line continue to becollinear after the transformation) and ratios of distances along aline. In one embodiment, a 3D affine transformation, specified by 12parameters, may be utilized. Nine parameters of the affinetransformation specify the linear transformation (which may berepresented by a three by three matrix) and three parameters of theaffine transformation specify the 3D translation in each dimension. Theparameters of the affine transform that minimizes the misalignment ofthe boundary golden tibia region mapped into the target MRI scan datamay be stored.

Finally, operation 416 further refines the image registration of theboundary golden tibia region. In one embodiment, a spline transformationmay be used to register the coordinates of the boundary golden tibiaregion into the MRI scan data (target image space). In one embodiment, a3D B-Spline deformable transformation may be employed and thetransformation found in operation 414 may be used as the initialtransformation values for the 3D B-Spline deformable transformation.

A B-Spline deformable transformation typically is a free formdeformation of an object using a deformation field where a deformationvector is assigned to every point in space. In one embodiment, aB-Spline transformation may be specified with M×N parameters, where M isthe number of nodes in the B-Spline grid and N is the dimension of thespace. In one embodiment, a 3D B-Spline deformable transformation oforder three may be used to map every reference image 3D point into thetarget MRI scan by a different 3D vector. The field of the vectors maybe modeled using B-splines. Typically a grid J×K×L of control points maybe specified where J, K, and L are parameters of the transformation.

In one embodiment, splines of order three may be used with a grid 9×6×6of control points. That is, the transformation employs nine controlpoints in the medial/lateral direction (i.e., the x direction, and sixcontrol points in the other directions (i.e., the y and z directions).Three control points in each dimension (i.e., 3 of 9 in the x direction,3 of 6 in the y direction and 3 of 6 in the z direction) may be used tospecify boundary conditions. As such, the inner spline nodes may form agrid of size 6 by 3 by 3 and the boundary conditions increase the gridto size 9 by 6 by 6. The parametric set for this transformation has adimension of 3×9×6×6=972. The final parameters of the splinetransformation that minimizes the misalignment between the referencegolden tibia template and the target MRI scan data may be stored. Thismay be referred to as the cumulative tibia registration transformherein.

The shape of the tibia may vary more from patient to patient than doesthe shape of the femur. As a result, the affine transformation may notprovide a close enough registration of the golden tibia template to thetarget tibia in the target scan. This may cause the Splinetransformation to find a local optimum that may be far from the actualtibia in some areas. In one embodiment, an additional registrationoperation between the affine transform and spline transform operationsmay be performed to more closely align the golden tibia and the targettibia, allowing the spline transform to converge to the correct localoptimum rather than a nearby (but wrong) local optimum.

The class of transforms utilized generally should allow more flexibility(or degrees of freedom) than the Affine transform and less flexibilitythan the B-spline transforms. The number of degrees of freedom generallyis equal to the number of transform parameters. In one embodiment, aclass of transforms with more than 12 parameters and less than 3×9×6×6parameters may be used. For example, a B-spline transform with fewercontrol points (than used in the subsequent spline transform) may beused for the additional transform operation. Alternatively, thedeformations may be modeled using quadric rather than cubic functions.

In another embodiment, several golden tibia templates may be used thatrepresent typical tibia variations, e.g., golden tibia templates forvarum, valgum and normal tibia. In one embodiment, each of the goldentibia templates may be used during the translation, similarity andaffine transform registration operations to find the template thatprovides the best match (e.g., best correlation) in the affine transformregistration operation. This template may then be used in the remainingregistration operations.

Finally, in one embodiment, the tibia registration may be improved byperforming the tibia segmentation after the femur segmentation andadding a restriction on the tibia registration transformations such thatthe tibia may not penetrate the femur. In one embodiment, this may beimplemented by introducing a penalty for the penetration. In the targetMRI all the voxels that lie inside the femur splines may be marked. Themetric functions, described in more detail below, that are used in theregistration operations may be modified to include a penalty term. Thepenalty term may be computed by selecting a set of points on theboundary of the golden template segmentation, applying a transform tothe set of points (in a similar way as the transform is applied to thesample points used in the correlation computations), determining if atransformed sample point falls into any of the marked voxels, and addinga large value to the penalty term for each transformed sample point thatfalls into any of the marked voxels.

In each of the above registration operations, a metric may be used toquantify the degree of correspondence between features in both thereference image and target image achieved by a given transformation. Inone embodiment, the metric quantitatively measures how well thetransformed golden template image fits the target image (e.g., a targetMRI scan) and may compare the gray-scale intensity of the images using aset of sample points in the golden template region to be registered.

FIG. 20 depicts a flowchart illustrating one method for computing themetric used by the registration operations described above. For aparticular registration operation, the metric may be computed in thesame way, but the metric may have different parameters specified for theparticular registration operation. The metric may be referred to hereinas “local correlation in sample points.” Initially, operation 420selects a set of sample points in the golden template region to beregistered.

For the translation and similarity transformations, the sample pointsmay be selected as follows. Initially, a rectilinear grid of L×M×N thatcovers the whole bone in 3D space may be used. L, M, and N may vary fromone to 16. In one embodiment, an eight by eight grid in every imageslice may be used to select uniform sample points in the grown goldenregion of the golden template. For each grid cell, the first samplepoint is selected. If the sample point falls within the grown goldenregion, it is used. If the sample point falls outside the golden region,it is discarded.

For the affine and spline transformations, the sample points may bedetermined by randomly selecting one out of every 32 points in theboundary golden region of the MRI slice.

Next, operation 422 groups the selected points into buckets. In oneembodiment, buckets may be formed as follows. First, the 3D space may besubdivided into cells using a rectilinear grid. Sample points thatbelong to the same cell are placed in the same bucket. It should benoted that sample points may be grouped into buckets to compensate fornon-uniform intensities in the MRI scan.

For example, MRI scan data may be brighter in the middle of the imageand darker towards the edges of the image. This brightness gradienttypically is different for different scanners and may also depend onother parameters including elapsed time since the scanner was lastcalibrated. Additionally, high aspect ratio voxels typically result invoxel volume averaging. That is, cortical bone may appear very dark inareas where its surface is almost perpendicular to the slice andgenerally will not be averaged with nearby tissues. However, corticalbone may appear as light gray in the areas where its surface is almosttangent to the slice and generally may be averaged with a large amountof nearby tissues.

Next, operation 424 sub-samples the target MRI slice. Sub-sampling thetarget space generally has the effect of smoothing the metric function.This may remove tiny local minima such that the local minimizationalgorithm converges to a deeper minimum. In one embodiment, duringoperations 410 and 412 (of FIG. 19), each slice may be sub-sampled withan eight by eight grid. During operations 414 and 416 (of FIG. 19), eachslice may be sub-sampled with a four by four grid. That is, during thesub-sampling, one point from every grid cell may be selected (e.g., thefirst point) and the remaining points in the grid cells may bediscarded.

Next, operation 426 computes a correlation of the intensities of thepoints in each bucket and their corresponding points in the target MRIscan (after mapping). The correlation (NC) metric may be expressed as:

${{NC}\left( {A,B} \right)} = {\frac{\sum\limits_{i = 1}^{N}\; {A_{i}B_{i}}}{\sqrt{\left( {\sum\limits_{i = 1}^{N}\; A_{i}^{2}} \right)\left( {\sum\limits_{i = 1}^{N}\; B_{i}^{2}} \right)}}\frac{{N{\sum{A_{i}B_{i}}}} - {\sum{A_{i}{\sum B_{i}}}}}{\sqrt{{N{\sum A_{i}^{2}}} - \left( {\sum A_{i}} \right)^{2}}\sqrt{{N{\sum B_{i}^{2}}} - \left( {\sum B_{i}} \right)^{2}}}}$

where A_(i) is the intensity in the i^(th) voxel of image A, B_(i) isthe intensity in the corresponding i^(th) voxel of image B and N is thenumber of voxels considered, and the sum is taken from i equals one toN. It should be appreciated that the metric may be optimal when imagedifferences are minimized (or when the correlation of image similaritiesis maximized). The NC metric generally is insensitive to intensityshifts and to multiplicative factors between the two images and mayproduce a cost function with sharp peaks and well defined minima.

Finally, operation 428 averages the correlations computed in everybucket with weights proportional to the number of sample points in thebucket.

It is to be appreciated that the above process for computing the metricmay compensate for non-uniform intensities, for example, those describedabove with respect to FIGS. 3A-3C, in the MRI scan data.

During the registration process, an optimizer may be used to maximizeimage similarity between the reference image and target image byadjusting the parameters of a given transformation model to adjust thelocation of reference image coordinates in the target image. In oneembodiment, the optimizer for a registration operation may use thetransformed image (e.g., the transformed golden template) from theprevious registration operation as its initial approximation. Then,local optimization techniques may be used to search for a local optimumnear the initial starting approximation. This may be done so that anypotential matches farther away from the feature of interest (e.g., thefemur or tibia in a knee joint) reliably found in an earlier operationmay be eliminated.

For the translation transformation, an exhaustive search may beperformed using a grid 10×10×10 of size 5-millimeter translationvectors. A translation for every vector in the grid may be performed andthe translation providing a maximum local correlation in sample pointsmay be selected as the optimum translation.

For the similarity transformation, a regular step gradient descentoptimizer may be used by one embodiment. A regular step gradient descentoptimizer typically advances transformation parameters in the directionof the gradient and a bipartition scheme may be used to compute the stepsize. The gradient of a function typically points in the direction ofthe greatest rate of change and whose magnitude is equal to the greatestrate of change.

For example, the gradient for a three dimensional space may be given by:

${\nabla{f\left( {x,y,z} \right)}} = {\left( {\frac{\partial f}{\partial x},\frac{\partial f}{\partial y},\frac{\partial f}{\partial z}} \right).}$

That is, the gradient vector may be composed of partial derivatives ofthe metric function over all the parameters defining the transform. Inone embodiment the metric function may be a composition of an outer andN inner functions. The outer function may compute a metric valueaccording to operations 426 and 428 given the vectors {A_(i)} and{B_(i)}. The N inner functions may map N sample points from the fixed(reference) image A_(i) into the target image B_(i) using the transformand evaluate intensities of the target image B_(i) in the mapped points.Each of the inner functions generally depends on the transformparameters as well as on the point in the “from” space to which thetransform is applied. When computing the partial derivatives, the chainrule for computing a derivative of the function composition may be used.

To find a local minimum, parameter steps may be taken in the directionof the negative of the metric gradient (or the approximate gradient)over the transform parameter space at the current point. This generallyoptimizes the metric which typically has a local minimum when featuresof the reference image mapped into corresponding features of the targetimage have minimal misalignment).

The initial center of rotation for the similarity transformation (e.g.,operation 382 of FIG. 17) may be specified as the center of a boundingbox (or minimum sized cuboid with sides parallel to the coordinateplanes) that encloses the feature (e.g., a bone) registered in thetranslation registration (e.g., operation 380 of FIG. 17). Scalingcoefficients of approximately 40-millimeters may be used for the scalingparameters when bringing them together with translation parameters. Itis to be appreciated that the gradient computation generally assumesthat there is some metric function. With a similarity transformation,the transform parameters do not have the same dimensionality. Thetranslation parameters have a dimension of millimeters, while theparameters for rotational angles and scaling do not have a dimension ofmillimeters. In one embodiment, a metric M may be defined as

M=SQRT(X ² +Y ² +Z ²+(40-millimeter*A1)²+ . . . )

where X is the translation along the x axis, Y is the translation alongthe y axis, Z is the translation along the z axis, A1 is the firstrotation angle, etc. A scaling coefficient of approximately40-millimeters may be used because it is approximately half the size ofthe bone (in the anterior/posterior and medial/lateral directions) ofinterest and results in a point being moved approximately 40-millimeterswhen performing a rotation of one radian angle.

In one embodiment, a maximum move of 1.5-millimeters may be specifiedfor every point, a relaxation factor may be set to 0.98 and a maximum of300 iterations may be performed to determine the parameters of thesimilarity transformation that results in minimal misalignment betweenthe reference image and target MRI scan.

For the affine transformation, a regular step gradient optimizer may beused by one embodiment. Scaling coefficients of approximately40-millimeters may be used for the matrix coefficients variations whenbringing them together with translation parameters. A maximum1.0-millimeter move for every point may be set for each iteration, therelaxation factor may be set to 0.98 and a maximum of 300 iterations maybe performed to determine the parameters of the affine transformationthat results in minimal misalignment.

For the B-spline transformation, a modified regular step gradientdescent optimizer may be used by one embodiment when searching for thebest B-spline deformable transformation. An MRI image gradient may oftenfollow the bone surface in diseased areas (e.g., where the bone contactsurface is severely damaged and/or where osteophytes have grown). Such agradient may cause deformations of the golden template that wouldintroduce large distortions in the segmented bone shape.

In one embodiment, the MRI image gradient may be corrected for suchdeformations by computing a normal to golden boundary vector field whereevery vector points towards the closest point in the golden templateshape found during the affine transformation (e.g., operation 384 ofFIG. 17). This may be done using a distance map (also referred to as adistance transform). A distance map supplies each voxel of the imagewith the distance to the nearest obstacle voxel (e.g., a boundary voxelin a binary image). In one embodiment, the gradient of the signeddistance map of the golden tibia region may be mapped using the affinetransformation found in operation 384 of FIG. 17. In one embodiment, asigned Danielsson distance map image filter algorithm may be used. Then,the MRI image gradient may be projected onto the vector field to obtainthe corrected gradient field. This corrected gradient field is parallelto the normal to golden boundary field and typically defines a very thinsubset of the set of B-spline transformations that may be traveledduring the optimization.

Additionally, rather than computing one gradient vector for thetransform space and taking a step along it, a separate gradient may becomputed for every spline node. In one embodiment, order three B-splines(with J×K×L control nodes) may be used and J×K×L gradients may becomputed, one for each control point. At every iteration, each of thespline nodes may be moved along its respective gradient. This may allowthe spline curve to be moved in low contrast areas at the same time itis moved in high contrast areas. A relaxation factor of 0.95 may be usedfor each spline node. A maximum move of one-millimeter may be set forevery point during an iteration and a maximum of 20 iterations may beperformed to find the parameters of the B-spline transformation thatprovides minimal misalignment of the golden tibia region mapped into thetarget MRI scan.

Once the position and shape of the feature of interest of the joint hasbeen determined using image registration (operation 370 of FIG. 16), theregistration results may be refined using anchor segmentation and anchorregions (operation 372 of FIG. 16). FIG. 21 depicts a flowchartillustrating one method for refining the registration results usinganchor segmentation and anchor regions. Typically, during thisoperation, one more registration may be done using an artificiallygenerated image for the fixed image 394 of the registration framework390. Use of an artificial image may improve the overall segmentation byregistering known good regions that typically do not change from scan toscan to correct for any errors due to diseased and/or low contrast areasthat otherwise may distort the registration.

Additionally, the artificial image may be used to increase surfacedetection precision of articular surfaces and shaft middle regions. Theimage slices typically have higher resolution in two dimensions (e.g.,0.3-millimeter in the y and z dimensions) and lower resolution in thethird dimension (e.g., 2-millimeters in the x dimension). The articularsurfaces and shaft middle regions typically are well defined in theimage slices due to these surfaces generally being perpendicular to theslices. The surface detection precision may be improved using acombination of 2D and 3D techniques that preserves the in-sliceprecision by only moving points within slices rather than betweenslices. Further, a 3D B-spline transform may be used such that theslices are not deformed independently of one another. Since each slicemay not contain enough information, deforming each slice independentlymay result in the registration finding the wrong features. Instead, theslices as a whole may be deformed such that the registration remainsnear the desired feature. While each slice may be deformed differently,the difference in deformation between slices generally is small suchthat the changes from one slice to the next are gradual.

In one embodiment, the artificial image may comprise a set of dark andlight sample points that may be used by the metric. All dark points inthe artificial image may have the same intensity value (e.g., 100) andall light points in the artificial image may have the same intensityvalue (e.g., 200). It should be appreciated that the correlations aregenerally insensitive to scaling and zero shift. Thus, any intensityvalues may be used as long as the dark intensity value is less than thelight intensity value.

Initially, operation 430 may apply the cumulative registration transform(computed by operation 370 of FIG. 16) to an anchor segmentation meshand its three associated anchor region meshes (e.g., InDark-OutLightmesh, InLight-OutDark mesh and Dark-in-Light mesh) to generate atransformed anchor segmentation mesh and associated transformed anchorregion meshes (transformed InDark-OutLight anchor mesh, transformedInLight-OutDark anchor mesh and transformed Dark-in-Light anchor mesh)that lie in a space identical to the target image space.

Then, operation 432 generates random sample points lying within a thinvolume surrounding the transformed anchor segmentation mesh surface. Inone embodiment, this may be a volume having an outer boundary defined bythe anchor segmentation mesh surface plus 1.5-millimeters and an innerboundary defined by the anchor segmentation mesh surface minus1.5-millimeters, which may be referred to herein as the 1.5-millimeterneighborhood. The random sample points may be generated such that theyare within the image slices of the target scan but not between theslices. For example, the image slices may be transversal to the x-axiswith a spacing of 2-millimeters (at x-axis locations 0.0, 2.0, 4.0, . .. ). When a sample point is selected, its x-coordinate may be one of0.0, 2.0, 4.0, etc. but may not be 1.7, 3.0, or some non-multiple of2.0.

In one embodiment, voxels may be marked in every image slice that belongto the 1.5-millimeter neighborhood as follows. First, the intersectionof the transformed anchor mesh with every image slice may be found. Itshould be appreciated that the intersection of the anchor mesh with animage slice may be a polyline(s). Then, in each image slice, thepolyline segments may be traversed and all pixels that intersect withthe mesh may be marked. Next, a Dilate filter may be applied to themarked pixels of each image slice using a radius of 1.5-millimeters. TheDilate filter typically enlarges the marked region by adding all thepoints that lie within a 1.5-millimeter distance from the originallymarked points.

After operation 432, operation 434 determines if a sample point liesinside the transformed InDark-OutLight mesh surface. If operation 434determines that the sample point lies inside the transformedInDark-OutLight mesh surface, then operation 442 is performed. Ifoperation 434 determines that the sample point does not lie inside thetransformed InDark-OutLight mesh surface, then operation 436 isperformed.

Operation 442 determines if the sample point lies inside the transformedanchor segmentation mesh surface. If operation 442 determines that thesample point lies inside the transformed anchor segmentation meshsurface, then operation 446 is performed. If operation 442 determinesthat the sample point does not lie inside the transformed anchorsegmentation mesh surface, then operation 448 is performed.

Operation 436 determines if the sample point lies inside the transformedInLight-OutDark mesh surface. If operation 436 determines that thesample point lies inside the transformed InLight-OutDark mesh surface,then operation 444 is performed. If operation 436 determines that thesample point does not lie inside the transformed InLight-OutDark meshsurface, then operation 438 is performed.

Operation 444 determines if the sample point lies inside the transformedanchor segmentation mesh surface. If operation 444 determines that thesample point lies inside the transformed anchor segmentation meshsurface, then operation 448 is performed. If operation 444 determinessample point does not lie within the transformed anchor segmentationmesh surface, then operation 446 is performed.

Operation 438 determines if the sample point lies inside the transformedDark-In-Light mesh surface. If operation 438 determines that the samplepoint lies inside the transformed Dark-In-Light mesh surface, thenoperation 440 is performed. If operation 438 determines that the samplepoint does not lie inside the transformed Dark-In-Light mesh surface,then operation 450 is performed.

Operation 440 determines if the sample point is within 0.75-millimeterof the surface of the transformed anchor segmentation mesh. If operation440 determines that the sample point is within 0.75-millimeter of thesurface of the transformed anchor segmentation mesh, then operation 446is performed. If operation 440 determines that the sample point is notwithin 0.75-millimeter of the surface of the anchor segmentation mesh,then operation 450 is performed.

Operation 446 adds the sample point to the artificial image as a darkpoint. Then, operation 450 is performed.

Operation 448 adds the sample point to the artificial image as a lightsample point. Then, operation 450 is performed.

Operation 450 determines if there are more randomly generated samplespoints to be added to the artificial image. If operation 450 determinesthat there are more randomly generated sample points to be added to theartificial image, then operation 434 is performed. If operation 450determines that there are no more randomly generated sample points to beadded to the artificial image, then operation 452 is performed.

FIG. 22 depicts a set of randomly generated light sample points 460 anddark sample points 462 over the target MRI 464. In one embodiment,approximately 8,000 sample points (light and dark) may be generated overthe entire artificial image.

Referring again to FIG. 21, if operation 450 determines that there areno more randomly generated sample point to be added to the artificialimage, operation 452 registers the set of dark and light points to thetarget MRI scan. This operation may perform a registration similar tothe registration operation 196 (depicted in FIG. 17). In thistransformation, a subset of B-spline deformable transformations may beperformed that move points along their respective slices, but nottransversal to their respective slices.

In a B-spline deformable transform, a translation vector for everycontrol point (e.g., in the set of J×K×L control points) may bespecified. To specify a transform that moves any point in 3D space alongthe y and z slice coordinates but not along the x coordinate, arestriction on the choice of translation vectors in the control pointsmay be introduced. In one embodiment, only translation vectors with thex coordinate set equal to zero may be used to move points in the planeof the slice (e.g., the y and z directions) but not transversal to theslice (e.g., the x direction).

The use of anchor region meshes which typically are well pronounced inmost image scans, may reduce registration errors due to unhealthy areasand/or areas with minimal contrast differences between the feature to besegmented and surrounding image areas. For example, in the area where ahealthy bone normally has cartilage, a damaged bone may or may not havecartilage. If cartilage is present in this damaged bone region, the boneboundary separates the dark cortical bone from the gray cartilagematter. If cartilage is not present in this area of the damaged bone,there may be white liquid matter next to the dark cortical bone or theremay be another dark cortical bone next to the damage bone area. Thus,the interface from the cortical bone to the outside matter in thisregion of the damaged bone typically varies from MRI scan to MRI scan.In such areas, the interface between the cortical and the innercancellous bone may be used as an anchor region.

The use of a subset of B-Spline deformable transforms may reduce errorsdue to the 2-millimeter spacing between image slices.

FIG. 23 depicts a flowchart illustrating one method for generatingspline curves outlining the surface of a feature of interest in eachtarget MRI slice (e.g., operation 376 of FIG. 16). Initially, operation470 intersects the generated 3D mesh model of the feature surface with aslice of the target scan data. The intersection defines a polyline curveof the surface of the feature (e.g., bone) in each slice. Two or morepolyline curves may be generated in a slice when the bone is not verystraightly positioned with respect to the slice direction. In suchinstances, the intersection of the mesh with the slice plane maygenerate two or more polyline curves.

A polyline curve is a piecewise linear approximation to a curved featureshape. Generally, this curve should be easy to manipulate with a set ofcontrol points. The polyline curve may have many segments, making itmore difficult to manipulate the polyline curve (e.g., during operation254 or 260 of FIG. 6). One embodiment may generate one or more Kochaneksplines from the polyline curve. Each spline typically has a smallernumber of control points and typically fits the polyline curve withabout 0.2-millimeter deviation. Generally, a Kochanek spline may havemore control points along the high curvature regions of the polylinecurve and fewer control points along low curvature regions (i.e., wherethe curve tends to be flatter) of the polyline curve.

Once a polyline curve has been generated, operation 472 may compute apolyline parameterization, L_(i), as a function of the polyline'slength. FIG. 24 depicts a polyline curve 481 with n vertices, V₀, V₁, .. . V_(i−1), V_(i) . . . V_(n−1). Note that vertex V₀ follows vertexV_(n−1) to form a closed contour curve. The length of a segmentconnecting vertices Vi−1 and Vi may be denoted by ΔL_(i) such that thelength parameterization, L_(i), of the polyline at vertex V_(i) may beexpressed as:

L _(i) =ΔL ₀ +ΔL _(i) + . . . +ΔL _(i).

Next, operation 474 may compute a polyline parameterization, A_(i), as afunction of the polyline's tangent variation. The absolute value of theangle between a vector connecting vertices V_(i−1) and V_(i) and avector connecting vertices V_(i) and V_(i+1) may be denoted by ΔA_(i)such that the tangent variation parameter A_(i) at vertex V_(i) may beexpressed as:

A _(i) =ΔA ₀ +ΔA ₁ + . . . +ΔA _(i).

Then, operation 476 determines a weighted sum parameterization of thepolyline length and tangent variation parameterizations. In oneembodiment the weighted sum parameterization, W_(i), at vertex V_(i) maybe computed as:

W _(i) =α*L _(i) +β*A _(i)

where α may be set to 0.2 and β may be set to 0.8 in one embodiment.

Then, operation 478 may perform a uniform sampling of the polyline usingthe W parameterization results determined by operation 476. In oneembodiment, a spacing interval of approximately 3.7 of the W parametervalue may be used for positioning K new sample points. First, K may becomputed as follows:

K=ROUND(W _(n)/3.7+0.5).

That is, the W parameter value, which is the last computed value W_(n),may be divided by 3.7 and the result rounded up to the nearest integerto get the number of new sample points. Then, the spacing of the samplepoints, ΔW may be computed as:

ΔW=W _(n) /K.

Finally, the K new sample points, which are uniformly spaced, may bepositioned at intervals ΔW of the parameter W. The resulting samplepoints may be used as control points for the Kochanek splines to convertthe polyline into a spline. A Kochanek spline generally has a tension, abias and a continuity parameter that may be used to change the behaviorof the tangents. That is, a closed Kochanek spline with K control pointstypically is interpolated with K curve segments. Each segment has astarting point, an ending point, a starting tangent and an endingtangent. Generally, the tension parameter changes the length of thetangent vectors, the bias parameter changes the direction of the tangentvectors and the continuity parameter changes the sharpness in changebetween tangents. In certain embodiments, the tension, bias andcontinuity parameters may be set to zero to generate a Catmull-Romspline.

In one embodiment, operation 478 may perform a linear interpolation ofW_(i) and W_(i+1) to locate a sample point that lies between W_(i) andW_(i+1). The interpolated value of W may be used to determine thecorresponding sample location in the segment connecting vertices V_(i)and V_(i+1).

In certain embodiments, operation 478 may divide the W parameter valueby six to obtain the new number of sample points K. That is,

K=ROUND(W _(n)/6+0.5).

Then, a measure of closeness (i.e., how closely the spline follows thepolyline) may be computed as follows. First, the spline is sampled suchthat there are seven sample points in every arc of the spline (i.e., 7*Ksample points). Then, the sum of the squared distances of the samplepoints to the polyline may be computed. Next, the coordinates of the Kcontrol points are varied (i.e., two*K parameters). Then, a localoptimization algorithm is used to find the closest spline. If theclosest spline found during the optimization is not within a certainprecision (e.g., within approximately 0.4-millimeter of the polyline),then the number of control points, K, may be increased by one. The newnumber of control points may be uniformly distributed along the Wparameter, and another optimization performed to find the new closestspline. Generally one to two optimizations provide a spline that followsthe polyline with the desired degree of precision (e.g., withinapproximately 0.2-millimeter).

Finally, operation 480 determines if a spline curve(s) should begenerated for another image slice. If operation 480 determines that aspline curve should be generated for another slice, then operation 472is performed. If operation 480 determines that there are no more imageslices to be processed, the method terminates.

As discussed above, in one embodiment, the output of the segmentationmay be a triangular mesh (e.g., a 3D surface model) of the segmentedbone(s) of a joint (e.g., the femur and tibia of a knee joint). The meshgenerated generally represents a watertight surface that closely followsthe segmentation contour curves of the slices, smoothly interpolatesbetween the segmentation contour curves, and may have a low triangularcount.

In one embodiment, a triangular mesh may be generated as follows. Thesegmentation data may be represented in 3D using (x, y, z) coordinateswith the image slices transversal to the x direction. Thus, thesegmentation contours lie in yz planes with fixed values of x.Initially, an in-slice distance image may be computed for each segmentedslice. The value of each (y, z) pixel in an in-slice distance image isthe distance to the closest point in the contours when the point islocated inside one of the contours and is the inverse (i.e., negative)of the distance to the closest point in the contours when the point isoutside all of the contours.

Then, a marching cubes algorithm may be applied to the in-slice distanceimages to generate the mesh. The marching cubes algorithm is a computeralgorithm for extracting a polygonal mesh of an isosurface (i.e., thecontours) from a three-dimensional scalar field (or voxels). Thealgorithm typically proceeds through the voxels, taking eight neighborvoxels at a time (thus forming an imaginary cube) and determines thepolygon(s) needed to represent the part of the isosurface (i.e.,contour) that passes through the imaginary cube. The individual polygonsare then fused into the desired surface. The generated mesh generallypasses through the zero level of the signed distance function in eachslice such that the mesh lies close to the contours.

It is to be appreciated that the image resolution in the y and zdirections typically determines how well the zero level of the signeddistance function approximates the original contours and may alsodetermine the triangular count in the resulting mesh. In one embodiment,a voxel size of 1.5-millimeters in the y and z directions may be used.This typically yields deviations within 0.1-millimeter of the originalcontours and produces a smooth mesh.

In one embodiment, a smoothing operation may be performed in the xdirection (i.e., transversal to the image slices) to compensate forsurface waviness that may have been introduced when the automaticallygenerated contours were adjusted (e.g., during operation 260 of FIG. 6).Such waviness may occur in regions of an image slice where there isminimal contrast variation and the curve is positioned by thetechnician. Typically a smooth best guess mesh in uncertain areas may bedesired when generating a planning model that may be used to locate theposition of an implant. Alternatively, a smooth overestimation may bedesired in uncertain areas such as in an arthritic model used to createa jig.

In one embodiment, simple smoothing may be used and the amount ofsmoothing (i.e., how much a voxel value may be modified) may becontrolled by two user specified parameters, MaxUp and MaxDown. After anaverage is computed for a voxel, it is clamped using these values tolimit the amount of smoothing. The smoothing operation typically doesnot change the image much in areas where the image contrast is good. Forsmooth best guess averaging in uncertain areas, MaxUp and MaxDown mayeach be set to 1 millimeter. For smooth overestimation averaging inuncertain regions, MaxUp may be set to 2-millimeters and MaxDown may beset to 0-millimeter.

The operation of adjusting segments of the segmentation process will nowbe described with reference to FIG. 25, which depicts a flowchart forone method of adjusting segments (e.g., operation 254 or operation 260of the flowchart depicted in FIG. 6). In one embodiment, thesegmentation data may be manually adjusted by a trained techniciansitting in front of a computer 6 and visually observing theautomatically generated contour curves in the image slices on a computerscreen 9. By interacting with computer controls 11, the trainedtechnician may manually manipulate the contour curves. The trainedtechnician may visually observe all of the contours as a 3D surfacemodel to select an image slice for further examination.

Initially, in operation 482 a slice is selected for verification. In oneembodiment, the slice may be manually selected by a technician.

Next, operation 484 determines if the segmentation contour curve in theselected slice is good. If operation 484 determines that thesegmentation contour curve is good, then operation 494 is performed. Ifoperation 484 determines that the segmentation contour curve is notgood, then operation 486 is performed.

Operation 486 determines if the segmentation contour curve isapproximately correct. If operation 486 determines that the contourcurve is approximately correct, then operation 492 is performed.

In operation 492 incorrect points of the segmentation contour curve maybe repositioned. In one embodiment this may be performed manually by atrained technician. It is to be appreciated that it may be difficult forthe technician to determine where the correct contour curve should belocated in a particular slice. This may be due to missing or unclearbone boundaries and/or areas with little contrast to distinguish imagefeatures. In one embodiment, a compare function may be provided to allowthe technician to visually compare the contour curve in the currentslice with the contour curves in adjacent slices. FIG. 26 depicts animage showing the contour curve 510 (e.g., a spline curve) with controlpoints 512 of the contour curve 510 for the current image slice as wellthe contour curves 514, 516 of the previous and next image slices,respectively, superimposed on the current image slice.

It may be difficult to determine where the correct segmentation contourcurve should be located due to missing or unclear bone boundaries due tothe presence of unhealthy areas, areas with limited contrastdifferences, and/or voxel volume averaging. When visually comparingadjacent slices, the technician may visualize the data in 2D planes (xy,yz, and xz) and in 3D. In one embodiment, the technician may select anarea for examination by positioning a cross hair on a location in anywindow and clicking a mouse button to select that image point. The crosshair will be placed at the desired point and may be used to indicate thesame location when the data is visualized in each window.

The technician may use the spline control points to manipulate the shapeof the curve. This may be done by using a mouse to click on a controlpoint and dragging it to a desired location. Additionally, thetechnician may add or delete spline curve control points. This may bedone by using a mouse to select two existing control points betweenwhich a control point will be inserted or deleted. Alternatively, thetechnician may use a mouse cursor to point to the location on the curvewhere a control point is to be inserted. In one embodiment, by pressingthe letter I on a keyboard and then positioning the cursor at thedesired location, clicking the left mouse button will insert the controlpoint. A control point may be deleted by pressing the letter D on thekeyboard and then positioning the cursor over the desired control pointto be deleted. The selected control point will change color. Theselected control point will be deleted when the left mouse button isclicked.

Referring again to FIG. 25, if operation 486 determines that the contourcurve is not approximately correct, then operation 488 is performed todelete the curve. Then, operation 490 is performed.

Operation 490 generates a new segmentation contour curve for the imageslice. In one embodiment, a technician may use a spline draw tool toinsert a new spline curve. With the spline draw tool, the technician mayclick on consecutive points in the current slice to indicate where thespline curve should be located and a spline curve is generated thatpasses through all of the indicated points. A right mouse click may beused to connect the first and last points of the new spline curve.Alternatively, the technician may use a paste command to copy the splinecurve(s) from the previous slice into the current slice. The splinecontrol points may then be manipulated to adjust the spline curves tofollow the feature in the current image slice.

In another embodiment, a paste similar command may be used by thetechnician to copy the spline curve from the previous slice into thecurrent slice. Rather then pasting a copy of the spline curve from theprevious slice, the spline curve may be automatically modified to passthrough similar image features present in both slices. This may be doneby registering a region around the spline curve in the previous slicethat is from about 0.7-millimeter outside of the curve to about5.0-millimeter within the curve. Initially, this region is registeredusing an affine transformation. Then, the result of the affine transformmay be used as a starting value for a B-Spline deformabletransformation. The metric used for the transform may be the localcorrelation in sample points metric described previously. Typically,more sample points may be taken closer to the curve and fewer samplepoints taken farther away from the curve. Next, the spline controlpoints may be modified by applying the final transformation found to thespline control points. Additionally, the trained technician may adjustfrom zero to a few control points in areas where the bone boundarychanges a lot from the slice due to the bone being tangent to the sliceor in areas of limited contrast (e.g., where there is an osteophytegrowth). Then, operation 492 is performed.

Operation 494 determines if there are additional slices to be verified.If operation 494 determines that there are additional slices to beverified, operation 482 is performed.

If operation 494 determines that there are no more slices to beverified, then operation 496 is performed. Operation 496 generates a 3Dsurface model of the segmented bone.

Then, operation 498 determines if the 3D surface model is good. In oneembodiment, a technician may manually determine if the 3D surface modelis good. The technician may use a spline 3D visualization tool thatgenerates a slice visualization showing the voxels inside all of thesplines in 3D, as illustrated by the 3D shape 520 depicted in FIG. 27.This spline 3D visualization tool typically may be generated in realtime to provide interactive updates to the technician as the splinecurves are manually edited. Alternatively, a mesh visualization may begenerated in response to a technician command. The mesh visualizationtypically generates a smooth mesh that passes close to all the splinecurves, e.g., mesh 290 depicted in FIG. 9.

If operation 498 determines that the 3D model is not good, thenoperation 500 is performed. Operation 500 selects a slice lying in anarea where the 3D shape is not good. In one embodiment, a technician maymanually select the slice. Then, operation 482 is performed.

If operation 498 determines that the 3D model is good, then the methodterminates.

The 3D surface models of the lower end of the femur and the upper end ofthe tibia of a patient's knee may be used to create arthroplasty jigsand/or implants. For example, the models may be used to create femur andtibia jigs that can be used with a patient's femur and tibia asdisclosed in the various U.S. patent applications incorporated byreference herein in this Detailed Description and filed by Park and Parket al. Automatic segmentation of image data to generate 3D bone modelsmay reduce the overall time required to perform a reconstructive surgeryto repair a dysfunctional joint and may also provide improved patientoutcomes.

Although the present invention has been described with respect toparticular embodiments, it should be understood that changes to thedescribed embodiments and/or methods may be made yet still embraced byalternative embodiments of the invention. For example, certainembodiments may operate in conjunction with a MRI or a CT medicalimaging system. Yet other embodiments may omit or add operations to themethods and processes disclosed herein. Accordingly, the proper scope ofthe present invention is defined by the claims herein.

1. A method of partitioning an image of a bone into a plurality ofregions, the method comprising the acts of: obtaining a plurality ofvolumetric image slices of the bone; generating a plurality of splinecurves associated with the bone; verifying that at least one of theplurality of spline curves follow a surface of the bone; and creating athree dimensional (3D) mesh representation based upon the at least oneof the plurality of spline curves.
 2. The method of claim 1, furthercomprising the act of employing selective resolution over differentareas of the bone.
 3. The method of claim 1, wherein a uniform spacingbetween the plurality of slices is 2 millimeters.
 4. The method of claim1, wherein the plurality with slices are in the sagittal, transversal orcoronal directions.
 5. The method of claim 1, wherein the volumetricimage slices represent a portion of the bone that makes contact with asecond bone.
 6. The method of claim 1, wherein at least one image slicewithin the plurality includes voxels that are distorted through voxelvolume averaging.
 7. The method of claim 1, wherein the plurality ofspline curves define the bone's shape and orientation.
 8. The method ofclaim 1, wherein the method yields a planning model of the bone.
 9. Themethod of claim 1, wherein the act of verifying comprises the act ofexamining neighboring slices in the plurality.
 10. The method of claim1, further comprising the act of integrating a representation of a modelbone into the image of the bone.
 11. The method of claim 10, where therepresentation of the model bone has no damaged cancellous or corticalmatter.
 12. A method of generating a representation of a model bone, themethod comprising the acts of: obtaining an image scan of therepresentation as a plurality of slices; segmenting each slice in theplurality using one or more segmentation curves; generating a mesh ofthe representation; adjusting each slice in the plurality to includeareas where the boundary area of the bone is stable between successiveimage scans; and generating anchor segmentation such that the anchorsegmentation follows a boundary of the representation of the model bone.13. The method of claim 12, wherein the act of segmenting excludessegmenting cartilage associated with the representation of the modelbone.
 14. The method of claim 12, wherein the act of obtaining the imagescan occurs through the use of magnetic resonance imaging (MRI).
 15. Themethod of claim 12, wherein at least a portion of the representation ofthe model bone exists between two segmentation curves
 16. The method ofclaim 12, wherein Gaussian smoothing is employed.
 17. The method ofclaim 12, wherein the boundary of the representation of the model bonecomprises a boundary between cortical and cancellous bone matter. 18.The method of claim 12, further comprising the act of connecting aplurality of cortical and cancellous boundary area together to obtainthe anchor segmentation.
 19. The method of claim 18, wherein theplurality of cortical and cancellous boundaries are disjoint.
 20. Themethod of claim 12, wherein at least one cancellous voxel is within theanchor segmentation and at least one cortical voxel is external to theanchor segmentation.
 21. The method of claim 12, wherein osteophytes areexcluded from the anchor segmentation.
 22. The method of claim 12,wherein at least one cortical voxels lies along the boundary of therepresentation of the model bone and at least one cancellous voxel liesadjacent the boundary of the representation of the model bone.
 23. Themethod of claim 12, further comprising the act of generating a 3D meshrepresenting the representation of the model bone.
 24. The method ofclaim 12, further comprising the act of repositioning a point on the oneor more segmentation curves by comparing point placement of adjacentsegmentation curves.
 25. A method of segmenting a target bone using arepresentation of a model bone, the method comprising the acts of:registering a segmented form of the representation to an image scan ofthe target bone; refining the registration of the segmented form of therepresentation near a boundary of the target bone; generating a meshfrom the segmented form of the representation; and generating aplurality of spline curves that approximate the intersection of the meshand one or more slices from the image scan of the target bone.
 26. Themethod of claim 25, wherein the method is performed on a joint.
 27. Themethod of claim 25, further comprising applying a transform within oneor more slices.
 28. The method of claim 27, wherein the act of applyingthe transforms includes moving points within a plane of the slices butnot transversal to the slices.
 29. The method of claim 25, wherein theact of refining comprises averaging one or more voxels of adjacentslices.
 30. The method of claim 25, further comprising the act ofverifying that at least one of the plurality of spline curves follow asurface of the target bone.
 31. The method of claim 30, wherein the actof verifying comprises the act of comparing at least two adjacentslices.
 32. A method of mapping a representation of a model bone into animage scan of a target bone: registering a generated portion of therepresentation into the image scan of the target bone using atranslational transformation; registering the generated portion of therepresentation into the image scan of the target bone using a similaritytransformation; registering a boundary portion of the representationinto the image scan of the target bone using an affine transformation;and registering the boundary portion of the representation into theimage scan of the target bone using a spline transformation.
 33. Themethod of claim 32, further comprising the act of adjusting one or moreparameters of the translational transformation such that thetranslational transformation minimizes misalignment of therepresentation of the model bone and the image scan of the target bone.34. The method of claim 32, wherein the similarity transform includesseven factors that are varied to optimize match between therepresentation and the target bone.
 35. The method of claim 32, whereinthe spline transformation is a 3D B-spline transformation.
 36. Themethod of claim 32, wherein the translational transform occurs withoutrotating, scaling, or deforming the representation.
 37. The method ofclaim 32, wherein the representation is rotated, scaled, or deformedduring the similarity transform.
 38. The method of claim 32, wherein inthe event the bone is a tibia, the method further comprises the act ofusing at least one additional registration operation between the affineand spline transformations.
 39. The method of claim 38, wherein the atleast one additional registration operation uses transforms representedwith one or more quadratic functions.
 40. The method of claim 32,wherein the model bone is varus.
 41. The method of claim 32, wherein themodel bone is valgus.
 42. The method of claim 32, wherein the targetbone includes a knee joint and the acts of registering are performedsuch that a tibia does not penetrate a femur.
 43. The method of claim32, further comprising the act of introducing a penalty into the mappingin the event that the mapping causes the tibia to penetrate the femur.44. The method of claim 32, wherein a step gradient descent optimizer isused during the similarity transform, affine transform, and splinetransforms.
 45. The method of claim 32, wherein the translationaltransform includes using an exhaustive optimizer.
 46. The method ofclaim 32, further comprising the act of correcting for variationsbetween the representation of the model bone and the image scan of thetarget bone by supplying each voxel of the image scan with the distanceto the nearest boundary voxel.
 47. The method of claim 32, wherein thesimilarity transform utilizes, for a center position, a center of abounding box that encloses the target bone registered during translationregistration.
 48. A method for determining a degree of correspondencebetween an image of a target bone and a representation of a model bone,the method comprising the acts of: selecting a plurality of samplepoints in the representation of the model bone to be registered;partitioning the plurality of sample points into a plurality of groups;sampling the image of the target bone; determining a correlation ofvoxel intensities between the image of the target bone and therepresentation of the model bone for each group in the plurality; andaveraging the correlation determined for each group in the plurality.49. The method of claim 48, wherein the act of partitioning comprisessubdividing a three dimensional image space into one or more cells usinga rectilinear grid and wherein points belonging to the same cell areplaced into the same group in the plurality.
 50. The method of claim 48,wherein the sample points are partitioned into the one or more groups tocompensate for non-uniformities in the image of the target bone.
 51. Themethod of claim 50, wherein the non-uniformities vary between differentimaging technologies.
 52. The method of claim 48, wherein the act ofdetermining the correlation is performed according to the followingformula:${{NC}\left( {A,B} \right)} = {\frac{\sum\limits_{i = 1}^{N}\; {A_{i}B_{i}}}{\sqrt{\left( {\sum\limits_{i = 1}^{N}\; A_{i}^{2}} \right)\left( {\sum\limits_{i = 1}^{N}\; B_{i}^{2}} \right)}}\frac{{N{\sum{A_{i}B_{i}}}} - {\sum{A_{i}{\sum B_{i}}}}}{\sqrt{{N{\sum A_{i}^{2}}} - \left( {\sum A_{i}} \right)^{2}}\sqrt{{N{\sum B_{i}^{2}}} - \left( {\sum B_{i}} \right)^{2}}}}$wherein A_(i) is the intensity in the i^(th) voxel of the image of thetarget bone A, B_(i) is the intensity in the corresponding i^(th) voxelof the image of the representation of the model bone B and N is thenumber of voxels considered.
 53. The method of claim 48, wherein the actof averaging includes weighting correlations in relation to the numberof sample points in the group.
 54. A method for refining registration ofa representation of a model bone to a target bone, the method comprisingthe acts of: transforming an anchor segmentation mesh; generating aplurality of random points around the transformed anchor segmentationmesh; determining if each point in the plurality lies inside one or moreof the following meshes: InDark-OutLight, InLight-OutDark, orDark-In-Light determining whether one or more of the plurality of pointslie within a threshold distance of the surface of the transformed anchorsegmentation mesh; and adding each point in the plurality as a darkpoint or light point depending upon whether the point lies within theInDark-OutLight, InLight-OutDark, or Dark-In-Light meshes and todetermine of the point lies inside or outside of the transformed anchorsegmentation mesh.
 55. The method of claim 54, wherein the plurality ofpoints are within image slices.
 56. The method of claim 54, wherein theact of determining if each point lies within a threshold distancecomprises the acts of: intersecting the anchor segmentation mesh witheach image slice; noting one or more pixels of the image slice thatintersect with the anchor segmentation mesh; and filtering the one ormore pixels outside the threshold distance.
 57. The method of claim 56,wherein the act of filtering occurs using a Dilate filter.
 58. Themethod of claim 54, wherein the threshold distance is 0.75 millimeters.59. The method of claim 54, wherein the act of transforming comprisesthe act of generating one or more spline curves.
 60. A method forgenerating spline curves outlining the surface of a feature of interestof a target bone, the method comprising the acts of: intersecting a 3Dmesh model of the feature surface with one or more slices of targetdata, the intersection defining a polyline curve; paramaterizing thepolyline curve as a function of length and tangent variation;calculating a weighted sum of the length and tangent paramaterizations;and sampling the polyline using the results of the act of calculating.61. The method of claim 60, further comprising positioning a pluralityof new sample points using the results of the act of calculating. 62.The method of claim 61, wherein the new sample points are used as one ormore control points for the polyline curve.
 63. The method of claim 62,wherein the control points are for a Kochanek spline.
 64. A method ofgenerating a spline curve of a current slice of a bone, the methodcomprising the acts of: obtaining a spline curve from a previous slice;and modifying the spline curve from the previous slice to emphasize oneor more features present in both the current slice and the previousslice.
 65. The method of 64, wherein the act of modifying the splinecurve from the previous slice occurs automatically.
 66. The method ofclaim 65, wherein the act of modifying the spline curve from theprevious slice comprises registering a region around the spline curvefrom the previous slice, the region defined by one or more thresholdvalues.
 67. The method of claim 66, wherein the one or more thresholdvalues include about 0.7 millimeters outside the spline curve from theprevious slice to about 5.0 millimeters within the spline curve from theprevious slice.
 68. The method of claim 66, wherein the region isinitially registered using an affine transformation.
 69. The method ofclaim 68, wherein a result from the affine transformation is used as astarting value for a B-spline deformable transformation.
 70. The methodof claim 66, wherein a greater number of points are taken for computinga match between the current and previous slices as the region comescloser to the spline curve from the previous slice.
 71. The method ofclaim 69, further comprising the act of modifying one or more pointsfrom the spline from the previous slice by applying a finaltransformation.
 72. The method of claim 65, wherein a technicianmodifies one or more control points to the spline curve from theprevious slice in the event that the bone boundary changes due to thecurrent and previous slices being tangent to the bone boundary.
 73. Themethod of claim 65, wherein a technician modifies one or more controlpoints to the spline curve from the previous slice in the event that thebone boundary changes due to osteophyte growth.